Evolution, Medicine, and Public Health

The ongoing COVID-19 pandemic is still producing hundreds of thousands of cases worldwide. However, the currently dominant Omicron variant (and its sub-variants) have proven to be less virulent than previous dominant variants, resulting in proportionately fewer severe cases, hospitalizations and deaths. Nonetheless, a persistent concern is that new mutations of the SARS-CoV-2 virus may yet produce more virulent variants. In the present study we provide evidence supporting the hypothesis that this is unlikely, and that COVID-19 will remain a low-severity although highly transmissible disease. Three complementary pieces of evidence support our argument. First, empirical observations suggest that the transmission advantage that Omicron (sub)variants enjoy is in large part due to their cell tropism in the upper respiratory tract, which renders them less virulent. Second, when a negative link between transmissibility and virulence is included in agent-based epidemiological models, viruses evolve towards lower virulence. Third, genetic diversification of SARS-CoV-2 suggests that epistasis in the Omicron family reduces the diversity of successful variants. Taken together these observations point to a high likelihood that the severity of COVID-19 will remain sufficiently low for an endemic status to be reached, provided that vaccination campaigns and sensible hygiene and social measures continue worldwide, as suggested by the World Health Organization.

Society, culture, and individual motivations affect human decisions regarding their health behaviors and preventative care, and health-related perceptions and behaviors can change at the population level as cultures evolve. An increase in vaccine hesitancy, an individual mindset informed within a cultural context, has resulted in a decrease in vaccination coverage and an increase in vaccine-preventable disease (VPD) outbreaks, particularly in developed countries where vaccination rates are generally high. Understanding local vaccination cultures, which evolve through an interaction between beliefs and behaviors and are influenced by the broader cultural landscape, is critical to fostering public health. Vaccine mandates and vaccine inaccessibility are two external factors that interact with individual beliefs to affect vaccine-related behaviors. To better understand the population dynamics of vaccine hesitancy, it is important to study how these external factors could shape a populations vaccination decisions and affect the broader health culture. Using a mathematical model of cultural evolution, we explore the effects of vaccine mandates, vaccine inaccessibility, and varying cultural selection trajectories on a populations level of vaccine hesitancy and vaccination behavior. We show that vaccine mandates can lead to a phenomenon in which high vaccine hesitancy co-occurs with high vaccination coverage, and that high vaccine confidence can be maintained even in areas where access to vaccines is limited.

The progress of an epidemic in a small closed community is simulated by an agent-based model which allows vaccination and variation. The attributes of the virus are governed by two genetic loci: the P-locus, which determines growth, and the M-locus, which determines immune characteristics. Mutation at either locus modifies the attributes of the virus and leads to evolution through natural selection. For both loci the crucial variable is the potential mutation supply UPot, because evolution is likely to happen when UPot > 1. Mutation at the P-locus causes a limited increase in virulence, which may be affected by vaccine design. Mutation at the M-locus may cause a qualitative shift of dynamic regime from a simple limited epidemic to a perennial endemic disease by giving rise to escape mutants which may themselves mutate. A broad vaccine that remains efficacious despite several mutations at the M-locus prevents this shift and provides protection despite the evolution of the virus. Escape variants may nevertheless arise through recombination after coinfection, and can be suppressed by timely revaccination, using the prevalent strain to design the vaccine.

Health perceptions and health-related behaviors can change at the population level as cultures evolve. In the last decade, despite the proven efficacy of vaccines, the developed world has seen a resurgence of vaccine-preventable diseases (VPDs) such as measles, pertussis, and polio. Vaccine hesitancy, an individual attitude influenced by historical, political, and socio-cultural forces, is believed to be a primary factor responsible for decreasing vaccine coverage, thereby increasing the risk and occurrence of VPD outbreaks. In recent years, mathematical models of disease dynamics have begun to incorporate aspects of human behavior, however they do not address how beliefs and motivations influence these health behaviors. Here, using a mathematical modeling framework, we explore the effects of cultural evolution on vaccine hesitancy and vaccination behavior. With this model, we shed light on facets of cultural evolution (vertical and oblique transmission, homophily, etc.) that promote the spread of vaccine hesitancy, ultimately affecting levels of vaccination coverage and VPD outbreak risk in a population. In addition, we present our model as a generalizable framework for exploring cultural evolution when humans beliefs influence, but do not strictly dictate, their behaviors. This model offers a means of exploring how parents potentially conflicting beliefs and cultural traits could affect their childrens health and fitness. We show that vaccine confidence and vaccine-conferred benefits can both be driving forces of vaccine coverage. We also demonstrate that an assortative preference among vaccine-hesitant individuals can lead to increased vaccine hesitancy and lower vaccine coverage.

When several factors align, pathogens that normally infect wildlife can spill over into the human population. If pathogen transmission within the human population is self-sustaining, or rapidly evolves to be self sustaining, novel human pathogens can emerge. Although many factors influence the likelihood of spillover and emergence, the rate of contact between humans and wildlife is critical. Thus, for those pathogens inhabiting wildlife reservoirs with pronounced seasonal fluctuations in population density, it is broadly recognized that spillover risk also varies with season. What remains unknown, however, is the extent to which seasonal fluctuations in reservoir populations influence the evolutionary dynamics of pathogens in ways that affect the likelihood of emergence. Here, we use mathematical models and stochastic simulations to show that seasonal fluctuations in reservoir population densities lead to seasonal increases in genetic variation within pathogen populations and thus influence the waiting time for mutations capable of sustained human-to-human transmission. These seasonal increases in genetic variation also lead to elevated risk of emergence at predictable times of year.

Comparative oncology is a rapidly expanding field that seeks to explain variation in cancer risk across species by examining trends between tumour prevalence and key risk factors such as body mass, longevity, life history traits, and mutation rates. These trends are then used to address fundamental questions in the field, including the discovery of potential novel anti-cancer therapies, improvements to species conservation efforts, and understanding how cancer has influenced the evolution of multicellularity. They thus must be robust. This study demonstrates that when estimated on captive species those trends are heavily influenced by their scientific and public popularity, and that accounting for this bias can substantially alter their direction and magnitude. Hence, we reanalysed published captive vertebrate datasets examining the associations between neoplasia, malignancy, and lethal tumour prevalences with body mass, longevity, life history traits, and germinal cells mutation rates. When we included proxies of species popularity in our analyses, the previously reported weak effect of body mass on tumour and malignancy prevalences disappeared entirely. Similarly, the previously reported positive relationship between germline mutation rate and cancer mortality was eliminated after controlling for popularity bias. For life history traits, the effect of clutch size on cancer neoplasia and malignancy prevalences in birds doubled in magnitude, and while the negative trend between gestation length and tumour prevalence in mammals was not greatly affected, our analyses revealed that baseline tumour prevalences were underestimated for popular animals. Finally, the previously observed association between hemochorial placentation and cancer mortality in mammals was eliminated when confounding variables were included. Collectively, these results demonstrate that current comparative analyses based on tumour prevalence in captive animals are heavily influenced by species scientific and public popularities. Future studies utilising such datasets should incorporate measures of species popularity as confounding variables to ensure more robust conclusions and misleading research directions.

Group-living is one of the six major evolutionary transitions. However, group-living creates stresses that naturally cause group fragmentation, and hence loss of the benefits that group-living provides. How species that live in large groups counteract these forces is not well understood. I analyse comparative data on grooming networks from a large sample of primate species and show that two different social grades can be differentiated in terms of network size and structure. I show that living in large, stable groups involves a combination of increased investment in bonding behaviours (made possible by a dietary adjustment) and the evolution of neuronally expensive cognitive skills of the kind known to underpin social relationships in humans. The first allows the stresses created by these relationships to be defused; the second allows large numbers of weak relationships to be managed, creating a form of multilevel sociality based on strong versus weak ties similar to that found in human social networks.

Although the SARS-CoV-19 virus spread rapidly around in world in early 2020, disease epidemics in different places evolved differently as the year progressed - and the state of the COVID-19 pandemic now varies significantly across different countries and territories. We have created a taxonomy of possible categories of disease dynamics, and used the evolution of reported COVID-19 cases relative to changes in disease control measures, together with total reported cases and deaths, to allocate most countries and territories among the possible categories. As of 31 January 2021, we find that the disease was (1) kept out or suppressed quickly through quarantines and testing & tracing in 39 countries with 29 million people, (2) suppressed on one or more occasions through control measures in 74 countries with 2.49 billion people, (3) spread slowly but not suppressed, with cases still increasing or just past a peak, in 31 countries with 1.45 billion people, (4) spread through the population, but slowed a result of control measures, leading to a "flattened curve" and fewer infections than if the epidemic were unmitigated, in 32 countries with 2.24 billion people, and (5) spread through the population with some but limited mitigation in 5 countries with 168 million people. In addition, several countries have experienced increases in cases after disease appeared to have finished spreading due to declining numbers of susceptible people. For some of these countries - for example Kenya, Pakistan and Afghanistan - the resurgences can be explained by the relaxation of control measures (and may have been enhanced by disease spread in population segments that experienced lower infection levels during the first waves). For other countries, the resurgences point to the effects of new virus variants with higher transmissibility or immunity resistance - including most countries in Southern Africa (where the B.1.351 variant has been identified) and several countries in West Africa (potentially due to the B.1.1.7 or other variants). These findings are consistent with mounting evidence of high infection rates in several low- and middle-income countries, both from seroprevalence studies and estimates of actual deaths from COVID-19 combined with estimates of expected mortality rates. We estimate that 1.3-3.0 billion people, or 17-39% of the global population, have been infected by SARS-CoV-2 to date, and that at least 4.5 million people have died from COVID-19 - much higher than reported cases and deaths. Disease control policies and vaccination strategies should be designed based on the state of the COVID-19 epidemic in the population - and consequently may need to be different in different countries. Key Points The state of the COVID-19 pandemic varies significantly in different countries and territories around the world - and policies for disease control and vaccination will need to be tailored accordingly. In any epidemic, there are several possibilities for how the disease will spread over time - and our analysis finds that, in fact, as of 31 January 2021, there were many countries and territories in each of the main categories of COVID-19 epidemic dynamics that might have been expected: O_LIKept out or suppressed quickly through quarantines and testing & tracing - in 39 countries with 29 million people (0.4% of the global population), mostly small island states and a few countries in Southeast Asia. [Category H in the following map and table] C_LIO_LISuppressed through control measures (social distancing, hygiene and testing & tracing) -in 74 countries with 2.49 billion people (31.9% of global population), mostly in Europe, East Asia and the Pacific. [Categories F and G] C_LIO_LISpread slowly but not suppressed, with cases still increasing or just past a peak - in 31 countries with 1.45 billion people (18.6% of global population), including many countries in Latin America, Eastern Europe and the Middle East, as well as the United States and Russia. [Categories D and E] C_LIO_LISpread through the population, but slowed as a result of control measures, leading to a "flattened curve" and fewer infections than if the epidemic were unmitigated - in 32 countries with 2.24 billion people (28.8% of global population), mostly in South and Southeast Asia (including India) and Africa. [Category B] C_LIO_LISpread through the population with some but limited mitigation or "flattening the curve" -in 5 countries with 168 million people (2.2% of global population). [Category A] C_LIO_LIExperienced increases in cases after disease appeared to have finished spreading, which in some countries might have been solely due to relaxation of control measures (especially in wealthier population segments which experienced low infection levels during the first wave) - for example in Kenya and in Pakistan and some Central Asian countries - but which in some countries is likely to be due to new virus variants with higher transmissibility or immunity resistance - for example in most countries in Southern Africa and several in West Africa, and possibly also in parts of South and Central America. [Category J and many countries in Category K] C_LI These findings are backed up by mounting evidence of high infection rates in several low- and middle-income countries. Seroprevalence studies in Kenya, Nigeria, Pakistan and South Africa have reported finding antibodies for SARS-CoV-2 in large percentages of the studied populations - and suggest that current infection levels are likely above 50% in each country. Studies of actual deaths due to COVID-19, combined with estimates of expected mortality rates, similarly suggest that SARS-CoV-2 has, by now, infected more than half of the populations in Bolivia, Ecuador, Peru, Mexico, South Africa, Sudan, Syria, Yemen and Zambia. Countries of all income levels, and from all regions, appear in each of the main disease dynamics categories; however, there are clear income and geographical patterns in states of COVID-19 epidemics around the world. Most high-income countries have controlled the spread of SARS-CoV-2 through measures. Middle-income countries are spread across all categories, and account for 45 of the 63 countries which have slowed the disease significantly but not fully suppressed it. Some low-income have experienced largely unmitigated susceptibility-driven dynamics, while others have "flattened the curve" to varying degrees. We estimate that between one and two out of every five people globally has been infected by SARS-CoV-2 to date, and that at least 4.5 million people have died from COVID-19. Our estimate of total infections - 1.3-3.0 billion people, or 17-39% of the global population - is between 13 and 30 times the number of confirmed cases, and twice to four times as much as previous estimates of total infection numbers. We estimate that 4.6-10.0 million people have died from COVID-19, between 2.1 and 4.5 times the number of deaths attributed to COVID-19. An estimated 8.9-12.5 million lives remained at risk from COVID-19 as of the end of January 2021, prior to vaccination efforts - mainly in high-income countries (2.4-2.9 million), China (2.1 million) and India (1.7-2.9 million). Vaccinations, of course, have already started to reduce these numbers substantially. Our analytical approach is simple but useful - providing insight into the epidemic status even in many low-income countries with limited disease monitoring, and with potential to provide early warnings of significant new variants. We compare the evolution of reported cases with changes in stringency of disease control measures, and check that infection levels are plausible given total reported cases and deaths and the income level of the country. Anomalies in which changes in the evolution of reported cases cannot be explained by changes in the stringency index provide indications of possible significant variants of the virus. Up to the end of January, the data provide indications of the presence of significant new variants in: O_LIMost countries in Southern Africa (where the B.1.351 variant, with higher transmissibility and some resistance to immunity, was first identified in South Africa) C_LIO_LISeveral countries in West Africa (likely with higher transmissibility and resistance to immunity, possibly the B.1.1.7 variant, which was first identified in the UK and has been found in Ghana and Nigeria, or possibly a different variant). C_LI They also suggest, with less certainty, that the disease dynamics may be affected by new variants in several countries in South and Central America (perhaps the P.1 variant descended from the B.1.1.28 variant which was first identified as coming from the Brazilian Amazon). Different countries should adopt different disease control policies, according to the state of the COVID-19 epidemic in the population. O_LIFor countries that have kept the disease out or suppressed outbreaks through control measures, their measures need to be kept in place - and potentially strengthened especially in the face of higher-transmissibility variants - until vaccines have been widely administered. C_LIO_LIFor countries in which the disease is spreading slowly, full control measures should be maintained at least until new case numbers fully decline from the peak; later, it may be possible to relax some measures, but if measures are relaxed too soon or too much after cases peak, then significant further outbreaks can be expected (as has already happened in several such countries). C_LIO_LIFor countries in which cases have declined following a flattened curve, there may be room to relax control measures that have the greatest negative health, economic and social consequences - but the most effective control measures will need to be maintained (even when cases remain low for extended periods), and measures may need to be strengthened to tackle variants which higher transmissibility or ability to evade immune responses. C_LIO_LIFor countries in which the disease spread was largely unmitigated, many control measures could be relaxed for most people - although there may be risks if sizeable population segments have much lower infection levels than the general population or from variants with a high degree of immunity resistance. C_LI These findings may have implications for the optimal distribution of early batches of vaccines within countries. O_LIFor countries that have kept the disease out or suppressed outbreaks through control measures, vaccinations should be given first to frontline healthcare and essential workers and to elderly and vulnerable groups (starting with the oldest and most vulnerable). C_LIO_LIFor countries in which the disease is spreading slowly, detailed modelling should be done to determine whether the optimal strategy is to vaccinate at-risk groups first or to vaccinate key transmitters to halt the outbreak and "crush the curve" while waiting for further vaccine supplies to arrive. For any country choosing the key transmitter strategy - as Indonesia has done and has been suggested for the United States - it will be essential to maintain control measures, and to keep higher transmissibility variants out, or otherwise the benefits of a key transmitter vaccination strategy could be lost. C_LIO_LIFor countries in which the cases declined following a flattened curve, vaccinations should probably be given first to elderly and vulnerable groups, but the optimal strategy may switch to vaccinating key transmitters if there are resurgences in cases due to higher-transmissibility or immunity-resistant variants. C_LIO_LIFor countries in which the disease spread was largely unmitigated, vaccination should concentrate on elderly and vulnerable people, because population-level immunity already exists, and the greatest danger lies in vulnerable people becoming infected due to endemic SARS-CoV-2 from variants that will likely circulate over many years. C_LI These findings may also have implications for the optimal distribution of the first vaccines across countries. For most countries, the optimal allocation of vaccines doses is likely still to be according to population size - as current recommendations suggest. However, the global optimal allocation strategy might include providing somewhat greater supplies, during the next few months, to countries where using the vaccine to halt spread of the disease might be possible (provided that disease control measures are maintained in those countries). Vaccination strategies will need to account for current and potential future virus variants as well as the likelihood that immunity from vaccination will wane over time. Higher-transmissibility variants of the SARS-CoV-2 virus increase the urgency of distributing vaccines in countries which have controlled the disease to date, and may alter the optimal strategy for countries deciding between vaccination first of elderly and vulnerable people or of key transmitters. Immunity-resistant variants of the virus may reduce the effectiveness of current vaccines, but are not likely to negate fully the protection they offer. Immunity acquired through vaccination is likely to wane over time - like immunity acquired through infection. In many, perhaps most, countries, the time to vaccinate the whole population will exceed the timeframe in which immunity from vaccination wanes or new immunity-resistant variants emerge. Looking to the longer term, therefore, new virus variants and waning immunity are likely to necessitate re-vaccination (with vaccines tailored to the latest variants) on a regular basis - and the optimal long-term strategies for ongoing vaccination will vary widely across countries and will depend on many factors. O_FIG O_LINKSMALLFIG WIDTH=154 HEIGHT=200 SRC="FIGDIR/small/21252586v3_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@c39b02org.highwire.dtl.DTLVardef@1f5ccceorg.highwire.dtl.DTLVardef@590b3dorg.highwire.dtl.DTLVardef@1f0f1c5_HPS_FORMAT_FIGEXP M_FIG C_FIG O_FIG O_LINKSMALLFIG WIDTH=151 HEIGHT=200 SRC="FIGDIR/small/21252586v3_ufig2.gif" ALT="Figure 2"> View larger version (60K): org.highwire.dtl.DTLVardef@190e169org.highwire.dtl.DTLVardef@bec2a2org.highwire.dtl.DTLVardef@1dc3cf0org.highwire.dtl.DTLVardef@24d56b_HPS_FORMAT_FIGEXP M_FIG C_FIG SummaryThe state of the COVID-19 pandemic varies significantly in different countries and territories around the world - and policies for disease control and vaccination will need to be tailored accordingly. Although the SARS-CoV-19 virus spread rapidly around in world in early 2020, the state of disease epidemics in different countries diverged rapidly as the year progressed. Many high-income countries have had second or third waves; other countries have seen cases continue to increase gradually; still others have experienced declines in cases to low levels after peaks in mid-2020. Facing different situations, different countries might need to adopt different policies in the coming months, including different disease control measures and vaccination strategies. Each country needs to know its COVID-19 status. There are several possible courses that a disease epidemic can take in a population. The disease can spread rapidly until its runs out of people remaining to infect; the disease can be slowed with control measures but still spread until large numbers of people are infected and immune; the disease can be suppressed or "crushed" by control measures; or the disease can be kept out completely. More complex disease dynamics will occur when a virus mutates, if new variants evade immune responses in people already infected or spread faster than before, or if the disease spreads differently in different segments of a population. Our research suggests that different countries have experienced outbreaks in each of the main possible categories: O_LISusceptibility-Driven Dynamics with Limited Mitigation [Category A] - in which the disease spread until it infect most people and declined due to low susceptibility levels (i.e., low share of the population still able to be infected). C_LIO_LISusceptibility-Driven Dynamics Mitigated by Measures [Categories B and C] - in which the disease curve was "flattened" by control measures, but the disease still spread and declined after infecting large numbers of people (but fewer than if there were no mitigation). C_LIO_LISusceptibility-plus-Measures-Driven Dynamics [Categories D and E] - in which the disease was slowed significantly but not suppressed, i.e., the curve was "flattened" but not "crushed", and the disease is still spreading in the population. C_LIO_LIMeasures-Driven Dynamics [Categories F and G] - in which the disease has been constrained to date mainly through control measures (social distancing, hygiene and testing & tracing), but, of course, could spread again if measures are relaxed because only a minority of the population has been infected. C_LIO_LIIndex-Case-Control Dynamics [Category H] - in which the disease has been kept out or suppressed to date through strict control measures (especially quarantines and testing & tracing). C_LIO_LIComplex Disease Dynamics due to Differences Across Population Segments and/or New Variants [Categories I and J, and many countries in Category K] - in which the disease experienced an apparently susceptibility-driven curve but with low overall infection levels (i.e., share of population infected) because some segments of the population have not had many infections, or in which the disease later shows an unexpected resurgence, due to spreading within the previously less-affected population segments or due to the emergence of immunity-evading strains of the virus. C_LI From reported data on COVID-19 cases and disease control measures, we can categorize, for most countries and territories, the dynamics of the disease to date. First, we compare the timing of increases and/or decreases in reported new cases with the timing of changes in the "Stringency Index" of control measures compiled by the Oxford COVID-19 Government Response Tracker - and select the appropriate disease dynamics category. Second, we check if the infection levels expected for the category or categories indicated in the first step, are consistent with predicted ranges from the total numbers of reported cases and deaths using plausible ranges for the case detection rate, death detection rate and infection fatality ratio (IFR) given the countrys income level. This approach yields definitive categories for most countries and territories. COVID-19 disease dynamics are complicated in many countries, due to changes in control measures, seasonal patterns, geographical differences within countries, variability in case testing over time and emergence of new variants; such effects can be seen in the reported cases and deaths for many countries, but they do not obscure the basic drivers of disease dynamics - in other words, which of the categories applies - for most countries. The results suggest that there is a wide variation in the state of the COVID-19 epidemic around the world - as of 31 January 2021 - as illustrated in the map and the table below. COVID-19 has been suppressed through control measures - Categories F, G and H - in 113 countries and territories with 2.52 billion people or about 32.3% of the global population. However, the rest of the world are in different situations. A total of 31 countries, with populations of 1.45 billion people (18.6% of global population), fall into Categories D and E, meaning that the disease spread has been slowed but not suppressed and cases are currently still increasing or just past their peak. In the 23 countries of Category B, home to 2.05 billion people (26.3%), the disease was slowed but not suppressed, and cases have declined fully from the peak. In a further 9 countries with 0.19 billion people (2.5%), the disease spread through the population after initial waves were suppressed. COVID-19 outbreaks in 5 countries with 0.17 billion people (2.2%) were only somewhat mitigated by control measures and the virus has likely infected most of the population, falling into Category A. For several countries, which have apparent anomalies and fall outside the "basic" categories, the methodology provides important insights into epidemic status - pointing to situations where significant differences may exist across population segments or providing early warnings of new variants with higher transmissibility or resistance to immunity. For 5 Arabian Peninsula countries (3 of which are in Category I) and Singapore, it is likely that the virus has spread widely among migrant worker communities but has been controlled in the rest of the population. Categories J and K include 28 countries in which reported cases have surged after first waves which were likely or possibly susceptibility-driven, with curves flattened to various extents as a result of control measures which mitigated the epidemics. For some countries - including (1) Kenya, (2) Pakistan, Afghanistan, Kyrgyzstan and Kazakhstan, and (3) Egypt and Sudan - the second peaks are likely due to relaxation of measures but larger than might be expected due to disproportionate effects of the second waves on population segments (likely more affluent groups) which had lower infection levels during the first waves. For other countries - including (1) most countries in Southern Africa and (2) many countries in West Africa - the data suggest the presence of new virus variants with higher transmissibility and possible resistance to immunity, because resurgences or accelerations in cases happened in several neighbouring countries around the same time, and often without changes in control measures, and the second surges in cases usually involved faster increases than the first waves. The B.1.351 variant, with higher transmissibility and some resistance to immunity, was first identified in South Africa and is known to have caused most cases in the countrys second wave; the B.1.1.7 variant, which has higher transmissibility, has been found in Ghana and Nigeria. Several countries in South and Central America have experienced second waves or surges in cases: Surinames might be due to a higher-transmissibility variant (perhaps the P.1 variant that was first identified as coming from the Brazilian Amazon); Bolivias was large but could be explained by a significant decline in control measures; increases in Brazil and several other countries across South and Central America might simply be due to relaxation of social distancing behaviours over the Christmas and New Year holiday season although a role for virus variants cannot be discounted. Countries of all income levels appear in each of the main disease dynamics categories; however there are clear correlations between income groups and COVID-19 status categories. Most high-income countries have controlled the spread of SARS-CoV-2 through measures (and thus fall in Categories F, G and H). Middle-income countries are spread across all categories, and account for 45 of the 63 countries which have slowed the disease significantly but not fully suppressed it (Categories B, C, D and E). Some low-income countries have experienced largely unmitigated susceptibility-driven dynamics (Category A), while others have "flattened the curve" to varying degrees (Categories B, C, D and E). A mix of low- and middle-income countries are among the 34 countries in Categories J and K. Clear geographical patterns have emerged in the states of COVID-19 epidemics. There was more diversity in the state of the epidemic within regions earlier in the pandemic, but regional patterns had become clear by the end of January 2021. O_LIIn the Americas, the disease has spread slowly but has not been suppressed (Categories D and E) in most countries, including those with the largest populations, while many (but not all) of the Caribbean islands have kept SARS-CoV-2 out or under control (Category H). C_LIO_LIWestern and Northern European countries have, for the most part, controlled the disease through social distancing and hygiene measures, through two or three waves, and fall in Categories F and G. C_LIO_LIAcross Eastern Europe, the Levant, the Caucuses and Iran, all countries have constrained growth of the disease significantly, but infection levels in most have grown to moderate levels: different countries in these regions are included in Categories C, D/E and F/G, although their infection levels may all be in the moderate range. C_LIO_LIIn South and Central Asia, the virus has spread widely in most countries and cases have declined. In India, Bangladesh, Nepal and Uzbekistan, the case curve was flattened considerably, and current infection levels are likely moderate (Category B). In Pakistan, Afghanistan, Kyrgyzstan, and Kazakhstan (all in Category J), there have been two peaks in cases. Bhutan has contained the outbreaks of the virus to date (Category F). C_LIO_LIMany countries in East and South-East Asia have largely kept the disease under control or kept it out (Categories F, G and H). However, Malaysia, Mongolia and Myanmar experienced widespread outbreaks in the second half of 2020, the Philippines appears to be past the peak of its epidemic (Category B), and Indonesia has had a continuous but very slow rise in cases since the start of the pandemic (Category E). C_LIO_LIIn Australia, New Zealand and most Pacific Island States, SARS-CoV-2 has been excluded through quarantines, together with testing and tracing and lockdowns when the virus has spread beyond quarantined individuals (Categories F and H). C_LIO_LIAfrican countries appear to have differed greatly in how the disease has spread. Many countries appear to have experienced widespread epidemics followed by declines in case numbers, with varying degrees of "curve flattening" due to control measures (Categories A and B). In some countries - Tunisia, Libya, Togo, Botswana and Mozambique - cases spread very slowly (Categories D and E). A few countries appear to have kept the disease out, and a few others appear to have experienced full outbreaks after having previously kept the virus largely out. As described earlier, most countries in Southern Africa and many in West Africa experienced rapid growth in case numbers in December and January (putting many in Categories J and K) - suggestive of the presence of one or more new variants with higher transmissibility and possible resistance to immunity. C_LI We estimate that 1.3-3.0 billion people have been infected by SARS-CoV-2 to date, or about 17-39% of the global population. This estimate is between 13 and 30 times the number of confirmed cases, and perhaps twice to four times as much as previous estimates of total infection numbers. We estimate that 4.6-10.0 million people have died from COVID-19, between 2.1 and 4.5 times the number of deaths attributed to COVID-19. An estimated 8.9-12.5 million lives remain at risk from COVID-19, which can be saved through appropriate disease control measures and effective deployment of vaccines. Of these estimated extra deaths, if 90% of the population were to contract SARS-CoV-2, high-income countries account for about 2.4-2.9 million, China for about 2.1 million, and India for about 1.7- 2.9 million. Vaccinations, of course, have already started to reduce these numbers substantially. The findings of this report are backed up by mounting evidence of high infection rates in several low- and middle-income countries. Immunity testing provides direct evidence of the current state of the COVID-19 epidemic. Serological studies in several cities and regions in Brazil, India, Kenya, Pakistan, Qatar and South Africa have already reported finding antibodies for SARS-CoV-2 in large percentages of the studied populations. Note, however, that serological testing will underestimate the number of people who have been infected, due to waning of SARS-CoV-2 antibodies which affects significant numbers of people at about 4-6 months after infection. Consequently, serological testing might understate the actual degree of immunity in a population, because some people may have antibodies at levels below the detection threshold of the serology tests or may have memory B cell or T cell responses, either or both of which will likely reduce the severity of their illness if reinfected, and may reduce their vulnerability to reinfection and their likelihood to pass on the virus to other people if reinfected. In some places, reliable estimates of actual deaths due to COVID-19 may be a substitute for immunity testing to determine the share of population infected to date, at least approximately. Estimates, using a variety of methodologies, in Bolivia, Ecuador, Mexico, Peru, South Africa, Sudan, Syria, Yemen and Zambia all indicate that moderate to high shares of their populations have already been infected. Different countries should adopt different disease control policies, according to the state of the COVID-19 epidemic in the population. The following recommendations for countries in different categories take into account their current infection levels and the potential for additional infections if measures are relaxed or if new variants become common in a country. [tpltrtarr]Category A: Control measures should be relaxed for most people; such relaxation is not likely to lead to many more cases and deaths. In some low- and middle-income countries, wealthier population segments may have implemented greater degrees of social distancing during the epidemic to date, and have much lower infection levels than in the overall population; these segments should maintain social distancing, until vaccines arrive, because otherwise they could experience substantial outbreaks (which may have generated "second waves" in some countries). If and when new virus strains with higher transmissibility and/or resistance to immunity arrive, control measures should be strengthened again to avoid new outbreaks, if the new variants cause high mortality levels and if it seems likely that control measures will be more effective at controlling the new outbreaks than they were during the initial outbreaks. [tpltrtarr]Categories B and C: Control measures currently in place that have the greatest negative health, economic and social consequences could be relaxed. However, many control measures, especially the most effective in limiting virus spread, will need to be maintained, even though current case numbers are low; otherwise, significant resurgences can take place (as has happened, for instance, in Kenya and Bolivia). Population segments that may have maintained lower infection levels during the outbreak to date will need to maintain social distancing. If and when new virus strains with higher transmissibility and/or resistance to immunity arrive, control measures will likely have to be strengthened again to avoid new outbreaks. [tpltrtarr]Categories D and E: Control measures should be maintained at least until new case numbers fully decline from the peak; if measures are relaxed too soon after cases peak, then significant further outbreaks can be expected (as has happened, for instance, in Brazil, Colombia and Paraguay). Once cases fully decline from the peak - through further infections or as a result of vaccination programmes - then, and only then, some of the disease control measures with the greatest negative health, economic and social consequences could be relaxed. For some countries in Categories D and E, it may be possible to push R0_e below 1 and hence "crush the curve" by introducing some additional control measures or improving compliance with existing measures. New virus strains, especially with higher transmissibility, can generate resurgences or accelerations in growth of cases (as seen, for example, in Mozambique and Togo). [tpltrtarr]Categories F and G: COVID-19 control measures, put in place by governments and implemented by citizens, have saved perhaps 13.1-14.2 million lives. To continue to protect these lives, control measures need to be maintained until vaccines become widely available - and strengthened, if necessary, to compensate for new virus variants with higher transmissibility. [tpltrtarr]Category H: Measures to keep the disease out - mainly strict quarantines for new arrivals and testing & tracing of suspected cases - should be maintained until vaccines become widely available. The findings of this report may have implications for the optimal distribution of early batches of vaccines within countries. Current policies in several countries call for deployment of vaccines first to healthcare workers and then by age cohort, starting with the oldest. These plans are aligned with the results of modelling (by Imperial College London and others) which suggest that, when the supply of vaccines is limited, the optimal strategy is to target the elderly and other high-risk groups. However, the models indicate that, if the supply is sufficient to stop transmission of the virus, the optimal strategy switches to targeting key transmitters (e.g., working age people and potentially children) to indirectly protect the elderly and vulnerable. Consequently, the optimal strategy may vary according to the disease status category for each country: [tpltrtarr]Category A: Vaccination should concentrate on elderly and vulnerable people, starting with the oldest and most vulnerable. There is no alternative strategy to consider because population-level immunity already exists, and the greatest danger lies in vulnerable people becoming infected due to endemic SARS-CoV-2. [tpltrtarr]Categories B and C: Vaccinations should probably be given first to elderly and vulnerable groups, and to frontline healthcare and other essential workers. However, if there are resurgences in cases across the population due to higher-transmissibility or immunity-resistant variants, then the optimal strategy may switch to targeting key transmitters, similar to some countries in Categories D and E. [tpltrtarr]Categories D and E: In some of these countries, the optimal strategy may to be vaccinate key transmitters - while maintaining current disease control measures - because it may be possible to halt the outbreak and "crush the curve", while waiting for further vaccine supplies to arrive (after which disease control measures could be released). This strategy is being pursued by Indonesia and was suggested for the United States of America in a recent paper. However, careful modelling and planning would be necessary, for any country considering such an approach, to determine if a key transmitter strategy would in fact be optimal and if it would be feasible to implement. Further, for such a strategy to work, it will be necessary to keep control measures in place and to keep high-transmissibility variants of the virus out, until enough people have been vaccinated. [tpltrtarr]Categories F, G and H: Vaccinations should be given first to frontline healthcare and other essential workers and to elderly and vulnerable groups (starting with the oldest and most vulnerable). These findings may also have implications for the optimal distribution of the first vaccines across countries. Modelling by the Imperial College London COVID-19 Response Team suggests that the optimal allocation of vaccine doses among countries "is sensitive to many assumptions and will vary depending both on the vaccine characteristics and the stage of the epidemic in each country at vaccine introduction," and concluded that, "[g]iven this uncertainty, allocating vaccine doses according to population size appears to be the next most efficient approach." Our findings reinforce the uncertainty strongly: it is very likely that that stage of the epidemic varies greatly across countries. For most countries, the optimal allocation of vaccines doses is likely still to be according to population size - and then for those countries to give doses first to elderly and vulnerable people. However, the global optimal allocation strategy might include providing somewhat greater supplies, during the next few months, to Category D and E countries where using the vaccine to halt spread of the disease might be possible (provided that disease control measures are maintained in those countries). It is clear, in any case, that further modelling of vaccine allocation strategies is essential, taking into account the actual vaccine efficacies and projected available doses by month, as well as allowing for disease stage categories in different countries. Vaccination strategies will need to account for current and potential future virus variants as well as the likelihood that immunity from vaccination will wane over time. Higher-transmissibility variants of the SARS-CoV-2 virus increase the urgency of distributing vaccines, especially in Category F and G countries which may struggle to keep the disease suppressed, and might cause vaccination of key transmitters to be a less effective strategy for Category D and E countries if higher-transmissibility variants mean that they cant suppress the disease fully with limited vaccinations. Immunity-resistant variants of the virus may reduce the effectiveness of current vaccines, but are not likely to negate fully the protection offered by existing vaccines. Immunity acquired through vaccination is likely to wane over time - like immunity acquired through infection. Looking to the longer term, new virus variants and waning immunity are likely to necessitate re-vaccination (with vaccines effective against the latest variants) on a regular basis. In many, perhaps most, countries, the time to vaccinate the whole population will exceed the timeframe in which immunity from vaccination wanes or new immunity-resistant variants emerge. In making long-term plans, therefore, countries may face a wide range of options for who to vaccinate (elderly and vulnerable populations, key transmitters or entire populations) and for frequency of vaccination (every 6 months, annual, or once if residual benefits are sufficient). Optimal strategies for each country will be complicated to determine, as the choice will depend on many factors, including vaccine effectiveness in reducing mortality and in reducing transmission, how effectiveness wanes over time, mortality rates and transmissibility of new variants (in general and in previously infected or vaccinated people), and, once the risks to life and health from "endemic COVID" decrease to the point where COVID-19 is not an overriding issue, comparison with other health and budgetary priorities.

Toxoplasma gondii is widely reported to manipulate the behavior of its non-definitive hosts in ways that promote lethal interactions with the parasites definitive feline hosts. Nonetheless, there is a lack of data on the association between T. gondii infection and costly behavioral interactions with felids in nature. Here, we report that three decades of field observations reveal T. gondii infected hyena cubs approach lions more closely than uninfected peers and have higher rates of lion mortality. Our findings support the hypothesis that T. gondiis manipulation of host boldness is an extended phenotype that promotes parasite transmission from intermediate hosts to feline predators. While upregulating hyena boldness toward lions might achieve this, it may also reflect a collateral influence of manipulative traits that evolved in other hosts (e.g., rodents). In either case, our findings corroborate the potential impacts of a globally distributed and generalist parasite (T. gondii) on fitness-related interaction with felids in a wild host. One Sentence SummaryWild hyenas infected with the parasite T. gondii show evidence of costly behavioral manipulation when interacting with lions.

BackgroundMasking serves an important role in reducing the transmission of respiratory viruses, including SARS-CoV-2. During the COVID-19 pandemic, several perspective and review articles have also argued that masking reduces the risk of developing severe disease by reducing the inoculum dose received by the contact. This hypothesis - known as the variolation hypothesis - has gained considerable traction since its development. MethodsTo assess the plausibility of this hypothesis, we develop a quantitative framework for understanding the relationship between (i) inoculum dose and the risk of infection and (ii) inoculum dose and the risk of developing severe disease. We parameterize the mathematical models underlying this framework with parameters relevant for SARS-CoV-2 to quantify these relationships empirically and to gauge the range of inoculum doses in natural infections. We then identify and analyze relevant experimental studies of SARS-CoV-2 to ascertain the extent of empirical support for the proposed framework. ResultsMathematical models, when simulated under parameter values appropriate for SARS-CoV-2, indicate that the risk of infection and the risk of developing severe disease both increase with an increase in inoculum dose. However, the risk of infection increases from low to almost certain infection at low inoculum doses (with <1000 initially infected cells). In contrast, the risk of developing severe disease is only sensitive to dose at very high inoculum levels, above 106 initially infected cells. By drawing on studies that have estimated transmission bottleneck sizes of SARS-CoV-2, we find that inoculum doses are low in natural SARS-CoV-2 infections. As such, reductions in inoculum dose through masking or greater social distancing are expected to reduce the risk of infection but not the risk of developing severe disease conditional on infection. Our review of existing experimental studies support this finding. ConclusionsWe find that masking and other measures such as distancing that act to reduce inoculum doses in natural infections are highly unlikely to impact the contacts risk of developing severe disease conditional on infection. However, in support of existing empirical studies, we find that masking and other mitigation measures that reduce inoculum dose are expected to reduce the risk of infection with SARS-CoV-2. Our findings therefore undermine the plausibility of the variolation hypothesis, underscoring the need to focus on other factors such as comorbidities and host age for understanding the heterogeneity in disease outcomes for SARS-CoV-2.

It is widely believed that the herd immunity threshold (HIT) required to prevent a resurgence of SARS-CoV-2 is in excess of 50% for any epidemiological setting. Here, we demonstrate that HIT may be greatly reduced if a fraction of the population is unable to transmit the virus due to innate resistance or cross-protection from exposure to seasonal coronaviruses. The drop in HIT is proportional to the fraction of the population resistant only when that fraction is effectively segregated from the general population; however, when mixing is random, the drop in HIT is more precipitous. Significant reductions in expected mortality can also be observed in settings where a fraction of the population is resistant to infection. These results help to explain the large degree of regional variation observed in seroprevalence and cumulative deaths and suggest that sufficient herd-immunity may already be in place to substantially mitigate a potential second wave.

Pathogen adaptation to public health interventions, such as vaccination, may take tortuous routes and involve multiple mutations at distinct locations in the pathogen genome, acting on distinct phenotypic traits. Despite its importance for public health, how these multilocus adaptations jointly evolve is poorly understood. Here we consider the joint evolution of two adaptations: the pathogens ability to escape the vaccine-induced immune response and adjustments to the pathogens virulence and transmissi-bility. We elucidate the role played by epistasis and recombination, with an emphasis on the different protective effects of vaccination. We show that vaccines reducing transmission and/or increasing clearance generate positive epistasis between the vaccine-escape and virulence alleles, favouring strains that carry both mutations, whereas vaccines reducing virulence mortality generate negative epistasis, favouring strains that carry either mutation, but not both. High rates of recombination can affect these predictions. If epistasis is positive, frequent recombination can lead to the sequential fixation of the two mutations and prevent the transient build-up of more virulent escape strains. If epistasis is negative, frequent recombination between loci can create an evolutionary bistability, such that whichever adaptation is more accessible tends to be favoured in the long-term. Our work provides a timely alternative to the variant-centered perspective on pathogen adaptation and captures the effect of different types of vaccines on the interference between multiple adaptive mutations.

Comorbidity and its association with age are of great interest in geroscience. However, there are few model organisms that are well-suited to study comorbidities that will have high relevance to humans. In this light, we turn our attention to the companion dog. The companion dog shares many morbidities with humans. Thus, a better understanding of canine comorbidity relationships could benefit both humans and dogs. We present an analysis of canine comorbidity networks from the Dog Aging Project, a large epidemiological cohort study of companion dogs in the United States. We included owner-reported health conditions that occurred in at least 60 dogs (n=160) and included only dogs that had at least one of those health conditions (n=26,614). We constructed an undirected comorbidity network using a Poisson binomial test, adjusting for age, sex, sterilization status, breed background (i.e., purebred vs. mixed-breed), and weight. The comorbidity network reveals well-documented comorbidities, such as diabetes with cataracts and blindness, and hypertension with chronic kidney disease (CKD). In addition, this network also supports less well-studied comorbidity relationships, such as proteinuria with anemia. A directed comorbidity network accounting for time of reported condition onset suggests that diabetes precedes cataracts, elbow/hip dysplasia before osteoarthritis, and keratoconjunctivitis sicca before corneal ulcer, which are consistent with the canine literature. Analysis of age-stratified networks reveals that global centrality measures increase with age and are the highest in the Senior group compared to the Young Adult and Mature Adult groups. Only the Senior group identified the association between hypertension and CKD. Our results suggest that comorbidity network analysis is a promising method to enhance clinical knowledge and canine healthcare management. Author SummaryCompanion dogs age alongside humans and suffer many of the same diseases, making them an ideal "real-world" model for human health. Using owner-reported data from 26,614 dogs enrolled in the nationwide Dog Aging Project, we built the first large-scale maps--called comorbidity networks--that show which canine diseases tend to appear together and in what order. The networks correctly highlighted well-known pairings such as diabetes with cataracts and blindness, and hypertension with chronic kidney disease. They also revealed under-appreciated links--for example, protein loss in urine associated with anaemia--suggesting new avenues for veterinary research and care. By adding the reported date of diagnosis, we could infer likely sequences of the diseases: diabetes generally preceded cataracts, hip dysplasia came before osteoarthritis, and dry-eye disease often led to corneal ulcers. When we split the data by life stage, we saw disease webs become denser and more centred on a few key conditions as dogs grew older, echoing patterns seen in people. Together, these findings show that network analysis of large pet-health datasets can guide clinicians, inform breeding and prevention strategies, and ultimately improve the wellbeing of both dogs and humans.

The fitness advantage of an emerging pathogen variant is typically estimated from its change in frequency over time. This approach relies on the assumption that the pathogen spreads in a well-mixed population, where frequency changes are solely driven by selection. Yet, spatial structure can have major consequences on the spread of new variants. Here we model the change in frequency across time and across space of a new pathogen variant spreading in a two-patch host metapopulation. Crucially, we show that even small rates of migration can interfere with selection and may bias the estimation of fitness. We illustrate this effect with the evolution of SARS-CoV-2 by contrasting the spread of the Alpha and the Delta variants in England. We contend that the observed heterogeneity of fitness estimates across space could result from the influence of pathogen migration. This work highlights the need of a comprehensive theoretical framework accounting for the interplay between selection and migration on the spread of new pathogen variants.

Coinfection is common in wild animals, and can profoundly influence disease outcomes and transmission. However, most coinfection research is based on laboratory experiments or a few well-studied wildlife systems. Here, we use data from the PREDICT project - the largest standardized wildlife disease surveillance project ever conducted - to describe patterns of viral coinfection and evaluate their association with host and virus factors. Within the viruses prioritized for testing, we find that coinfection is rare (detected in just 223 of 65,662 animals), but still more common than expected by chance - especially coinfections of coronaviruses, paramyxoviruses, and influenza A virus. We further find that coinfection is associated with host age, but bats and rodents exhibit opposing relationships. We find that captive wild animals (specifically rats and mallards) exhibit higher coinfection than free-ranging wild animals, highlighting potential risks to wildlife and human health from the wildlife trade. Finally, we find that biases in sampling and testing can shape observed patterns of coinfection. Our results characterize associations among viruses in the largest relevant dataset currently available. Factors associated with coinfection in this dataset such as host-level variation, virus-virus interactions, and human interference should be further tested via surveillance and laboratory approaches to better resolve the role of coinfection as an important and likely overlooked driver of viral dynamics in nature.

1RNA viruses have high mutation rates, with the majority of mutations being deleterious. We examine patterns of deleterious mutation accumulation over multiple rounds of viral replication, with a focus on how cellular coinfection and heterogeneity in viral output affect these patterns. Specifically, using agentbased intercellular simulations we find, in agreement with previous studies, that coinfection of cells by viruses relaxes the strength of purifying selection, and thereby increases the rate of deleterious mutation accumulation. We further find that cellular heterogeneity in viral output exacerbates the rate of deleterious mutation accumulation, regardless of whether this heterogeneity in viral output is stochastic or is due to variation in cellular multiplicity of infection. These results highlight the need to consider the unique life histories of viruses and their population structure to better understand observed patterns of viral evolution.

Health officials warn that SARS-CoV-2 vaccines must be uniformly distributed within and among countries if we are to quell the ongoing pandemic. Yet there has been little critical assessment of the underlying reasons for this warning. Here, we explicitly show why vaccine equity is necessary. Perhaps counter-intuitively, we find that vaccine escape mutants are less likely to come from highly vaccinated regions where there is strong selection pressure favoring vaccine escape and more likely to come from neighboring unvaccinated regions where there is no selection favoring escape. Unvaccinated geographic regions thus provide evolutionary reservoirs from which new strains can arise and cause new epidemics within neighboring vaccinated regions and beyond. Our findings have timely implications for vaccine rollout strategies and public health policy.

Recombination (including reassortment) is a salient force in viral evolution, which has been implicated in the emergence of several zoonotic pathogens in human populations. Viral recombination occurs during simultaneous infection of an individual host with multiple strains (co-infection). This means that processes which affect the incidence of a disease in the host population affect how often viral genotypes recombine. We investigate whether and how host traits influence the rate of viral recombination using a mathematical model that makes feedbacks between viral evolution and host ecology explicit. Using approximations from population genetics, we find that viruses of host species that are short-lived, acutely infected, or whose immunity wanes quickly recombine more frequently than those of hosts that are relatively long-lived, chronically infected, and have long-lasting immunity. This is because of differences in the density of (co-)infections at equilibrium. Using highly pathogenic avian influenza sequence data we test the prediction that recombination is elevated in short-lived hosts. In agreement with this prediction, the magnitude of statistical associations between mutations on different segments of the flu genome increases with host body size, a proxy for lifespan. We discuss the implications of these findings for emergence.

Pathogens experience selection at multiple scales, given the need to transmit between hosts and replicate within them. This presents the challenge of cross-scale selective conflict when adaptations to one scale compromise fitness at another, such as mutations that improve transmissibility but make individuals less competitive within hosts. Selection operates differently at these scales, with tight transmission bottlenecks subjecting pathogen populations to genetic drift, and large population sizes within hosts enabling efficient selection for beneficial mutations. Compounding the reduction in diversity by transmission bottlenecks is the occupant-intruder competitive strategy exhibited by some pathogens, where the first variant to colonize a host prevents later arriving variants from contributing to infection, preventing immigration and turning transmission into a "founder takes all" contest. Here, we used multiple modeling approaches to examine how this behavior affects the efficiency of selection for both transmissibility and within- host fitness. We find that in the face of a trade-off, selection for transmissibility is maximized under a tight transmission bottleneck that minimizes within-host competition during colonization. While mutations with increased within-host fitness are favored during within-host replication, an occupant-intruder strategy prevents these mutants from displacing established residents and propagating across the host population, leading to their extinction if they are insufficiently transmissible. Finally, a model of competition on the scale of the host population revealed that competitive exclusion limits the propagation of mutations with improved within-host fitness, unless resident populations can incorporate alleles from intruding variants by recombination. Thus, competitive exclusion may facilitate the improvement and maintenance of pathogen transmissibility, with directional recombination allowing resident populations to mitigate the potential loss of within-host fitness imposed by this occupant-intruder strategy. Author SummaryTransmission is a defining feature of infectious diseases, and so a better understanding of how transmissibility evolves is important for improving disease surveillance and prevention. Successful transmission is often achieved by a small number of individuals which, after establishing residency in a host, may prevent newcomers from participating in infection. Here, we use modeling to examine how competitive exclusion of challengers by resident populations affects the balance between within-host competitive ability and transmissibility. We find that competitive exclusion strengthens selection for transmissibility by disproportionately benefitting the first variant to colonize a host and preventing mutants that may be more competitive but less transmissible from displacing established residents. Competitive exclusion also limits the propagation of mutants that improve within-host fitness without reducing transmissibility, however, increasing the advantage of recombination that allows resident populations to acquire beneficial alleles from challengers. Competitive strategies that allow pathogens to "claim ownership" of hosts may thus help pathogen populations maintain transmissibility, with genetic recombination facilitating within-host adaptation through the incorporation of beneficial alleles from challengers.

Multicellular organisms are confronted not only with germline mutations, but also mutations emerging in somatic cells. Somatic mutations can lead to various conditions, diseases, and cancers in particular. Somatic mutation rate is limited by evolved protection mechanisms, notably those repairing damaged DNA or eliminating mutated cells. However, in a broader context, life history traits such as body mass, age of first reproduction and reproductive lifespan, can also be subject to selection due to the negative fitness impacts of disease. Here, I analyze a simple coevolutionary model of somatic mutation rate (SMR) and fitness lifespan (hereafter called fitspan), the latter measured as the age at which inclusive fitness becomes negligible. Evolution in the model is driven by the fitness costs of disease, because as organisms age: disease is more extensive, disease prevention mechanisms are less effective and more costly, and fitness payoffs of disease prevention are lower. I investigate relations between selective forces and (co)evolutionary responses, notably showing the possibility of either monotone or oscillatory non-equilibrium dynamics and fast or slow returns to equilibrium. I then compare model predictions to recently published data on body mass, lifespan and somatic mutation rate. I show that the model (1) can explain the non-linear empirical relationship between somatic mutation and lifespan, (2) predicts the evolution of longer lifespans through a heretofore ignored feedback loop, and (3) the model is consistent with the idea that the linear relation between somatic mutation accumulation and age is the net result of mutational washing-out. I argue that the findings here generalize to other decreases in condition with age that are submitted to selection, including aging itself.