International Journal of Infectious Diseases

IntroductionThe Omicron BA.1/BA.2 wave in South Africa had lower hospitalisation and mortality than previous SARS-CoV-2 variants and was followed by an Omicron BA.4/BA.5 wave. This study compared admission incidence risk across waves, and the risk of mortality in the Omicron BA.4/BA.5 wave, to the Omicron BA.1/BA.2 and Delta waves. MethodsData from South Africas national hospital surveillance system, SARS-CoV-2 case linelist and Electronic Vaccine Data System were linked and analysed. Wave periods were defined when the country passed a weekly incidence of 30 cases/100,000 people. Mortality rates in the Delta, Omicron BA.1/BA.2 and Omicron BA.4/BA.5 wave periods were compared by post-imputation random effect multivariable logistic regression models. ResultsIn-hospital deaths declined 6-fold from 37,537 in the Delta wave to 6,074 in the Omicron BA.1/BA.2 wave and a further 7-fold to 837 in the Omicron BA.4/BA.5 wave. The case fatality ratio (CFR) was 25.9% (N=144,798), 10.9% (N=55,966) and 7.1% (N=11,860) in the Delta, Omicron BA.1/BA.2, and Omicron BA.4/BA.5 waves respectively. After adjusting for age, sex, race, comorbidities, health sector and province, compared to the Omicron BA.4/BA.5 wave, patients had higher risk of mortality in the Omicron BA.1/BA.2 wave (adjusted odds ratio [aOR] 1.43; 95% confidence interval [CI] 1.32-1.56) and Delta (aOR 3.22; 95% CI 2.98-3.49) wave. Being partially vaccinated (aOR 0.89, CI 0.86-0.93), fully vaccinated (aOR 0.63, CI 0.60-0.66) and boosted (aOR 0.31, CI 0.24-0.41); and prior laboratory-confirmed infection (aOR 0.38, CI 0.35-0.42) were associated with reduced risks of mortality. ConclusionOverall, admission incidence risk and in-hospital mortality, which had increased progressively in South Africas first three waves, decreased in the fourth Omicron BA.1/BA.2 wave and declined even further in the fifth Omicron BA.4/BA.5 wave. Mortality risk was lower in those with natural infection and vaccination, declining further as the number of vaccine doses increased.

Summary (abstract)O_ST_ABSBackgroundC_ST_ABSIn August 2023, an outbreak of mpox was reported in the eastern part, South Kivu Province, of Democratic Republic of the Congo. In this study, we aimed to investigate the origin of this outbreak and to assess how monkeypox virus spread among humans in the city of Kamituga. MethodsWe performed an observational cohort study by recruiting hospitalized patients with mpox-like symptoms. Furthermore, we compared structured, de-identified case report forms and interviews were conducted to determine the possible origins and modes of transmission of the mpox outbreak. We describe the clinical characteristics and epidemiology observed in reported infections. FindingsDuring the study period (24 September 2023 to 29 January 2024), 164 patients were admitted to the Kamituga hospital, 51 individuals were enrolled in the study and interviewed, and 37 (73%) of 51 individuals received a molecularly confirmed mpox diagnosis. The median age for males was 24 years (IQR 18-30; range 14-36) and 19 years for females (IQR 17-21; range 1-59). The cohort was comprised of 47 (92%) of 51 individuals who identified as heterosexual, and two (4%) of 51 as bisexual, with 31 (61%) of 51 individuals sexually active with more than one partner within the last six months. The direct transmission routes are unknown; however, it is expected that the majority of infections were transmitted via occupational exposures. Out of the 51 individuals, 24 (47%) were professional sex workers (PSWs), while five (10%) were gold miners, 6 (12%) were students, and four (8%) were farmers; the remaining individual occupations were unknown. The most common symptoms associated with clinical mpox diagnosis were fever, which was described in 38 (75%) of 51 individuals, and rash, which was described in 45 (88%) of 51 individuals. Among those with a rash, 21 (41%) of 51 individuals experienced oral lesions, and 32 (63%) of 51 presented anogenital lesions. Mpox viral DNA was detected by qPCR from vaginal, penile, and oral swabs in 37 (73%) of 51 enrolled individuals. Two deaths were reported. InterpretationIn this observational cohort study, mpox virus infection caused symptoms in a wide age range of participants with most cases presenting in sexually active individuals. Symptoms included fever, cough, lymphadenopathy, sore throat, chills, headache, back pain, muscle pain, vomiting, nausea, conjunctivitis, and rash (oral and anogenital). Heterosexual partners dominated human-to-human contact transmission suggesting that heterosexual close contact is the main form of transmission in this outbreak. Furthermore, Professional Sex Workers (PSWs) were the dominant occupation among infected individuals, indicating that PSWs and clients may be at higher risk for developing mpox virus infections.

BackgroundAfrica continues to have a significant public health problem with mpox, where endemic transmission persists and overlaps with a high burden of other infectious diseases. Although their epidemiology and clinical impact are still poorly understood throughout the continent, coinfections with human immunodeficiency virus (HIV) and varicella-zoster virus (VZV) can affect the clinical picture, severity of the disease, and accuracy of the diagnosis. MethodsFollowing PRISMA criteria, we registered the protocol in PROSPERO (CRD420251133960) and carried out a methodical review and meta-analysis. Through searches of numerous electronic databases and grey literature up to February 27, 2025, observational studies documenting mpox coinfections with VZV and/or HIV and related clinical symptoms in Africa were searched. Pooled prevalence was calculated using random-effects models. Subgroup analyses and meta-regression were conducted to investigate heterogeneity across WHO regions, countries, study designs, settings and type of participants. ResultsA total of 27 studies carried out across African countries were included. The pooled prevalence of VZV-mpox coinfection was 10.23% (95% CI: 2.95-29.93), while HIV-mpox coinfection prevalence was 6.55% (95% CI: 2.36-16.90), both of which had significant heterogeneity. Coinfections were far more prevalent in hospital-based environments than in community-based research. The rash was nearly universal throughout all clades, but the clinical manifestations varied depending on the viral clade, with clades I and Ia linked to more severe systemic symptoms than clade II. DiscussionsHIV and VZV coinfections with mpox pose a major yet possibly underestimated burden in Africa and are linked to more severe clinical presentations, particularly in hospital environments. The necessity of including clinical, epidemiological, and genomic data into mpox monitoring systems is emphasized by the observed clinical differences between clades. Improving patient management and outbreak preparedness across the continent requires strengthening diagnostic capacity and routinely screening for coinfections.

COVID-19 (Corona Virus) is caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-COV-2). The virus that was first discovered in China Wuhan Province about 3 months ago (first cases were reported in Wuhan on December 31st 2019) has spread world wide. The six (6) top countries (excluding China) most affected so far include; USA, Italy, Spain, Germany, France and Iran. With Italy showing the highest death toll. In Uganda where it was discovered on 19/3/2020 with one (01) case has just in nine (9) days, increased to thirty (30) infected individuals. This model is a wake up call over the rate at which COVID-19 is likely to spread throughout the country. Thus it is a guide for policymakers and planners to benchmark on for solutions to this deadly virus.

SARS-CoV-2 infection has been playing havoc with emerging omicron variants of concern (VoC). Here, we report sequencing of the omicron variant in 13 patients from India using Oxford Nanopore Technology (ONT) Minion, wherein a rapid amplicon based sequence analysis was performed to assess and compare with existing 34 mutations in spike glycoprotein. We highlight and discuss the nature of these mutations that are unique and common to other populations. This is perhaps the first report on omicron variants from India using a long read sequencing chemistry.

Early data indicated that infection with Omicron BA.1 sub-lineage was associated with a lower risk of hospitalisation and severe illness, compared to Delta infection. Recently, the BA.2 sub-lineage has increased in many areas globally. We aimed to assess the severity of BA.2 infections compared to BA.1 in South Africa. We performed data linkages for (i) national COVID-19 case data, (ii) SARS-CoV-2 laboratory test data, and (iii) COVID-19 hospitalisations data, nationally. For cases identified using TaqPath COVID-19 PCR, infections were designated as S-gene target failure (SGTF, proxy for BA.1) or S-gene positive (proxy for BA.2). Disease severity was assessed using multivariable logistic regression models comparing individuals with S-gene positive infection to SGTF-infected individuals diagnosed between 1 December 2021 to 20 January 2022. From week 49 (starting 5 December 2021) through week 4 (ending 29 January 2022), the proportion of S-gene positive infections increased from 3% (931/31,271) to 80% (2,425/3,031). The odds of being admitted to hospital did not differ between individuals with S-gene positive (BA.2 proxy) infection compared to SGTF (BA.1 proxy) infection (adjusted odds ratio (aOR) 0.96, 95% confidence interval (CI) 0.85-1.09). Among hospitalised individuals, after controlling for factors associated with severe disease, the odds of severe disease did not differ for individuals with S-gene positive infection compared to SGTF infection (aOR 0.91, 95%CI 0.68-1.22). These data suggest that while BA.2 may have a competitive advantage over BA.1 in some settings, the clinical profile of illness remains similar.

BackgroundIn September 2023, an mpox outbreak was reported in the eastern part, South Kivu Province, of Democratic Republic of the Congo. This outbreak is still ongoing and expanding to other regions and countries. Here, we describe the epidemiological and genomic evolution of the outbreak from September 2023 to June 2024. MethodsConsenting patients with mpox-like symptoms admitted to the Kamituga and the Kamanyola hospitals were recruited to the study. Samples from throat, lesions, breast milk and placenta were collected for PCR testing and sequencing. For the patients from Kamituga hospital, data on place of residence and possible exposures were collected by interviews. The location and numbers of employees were collected for all bars with sex workers. Where possible, exposures were linked to the genomic sequencing data for cluster analysis. FindingsIn total, 670 (suspected) mpox cases were admitted to the Kamituga hospital. There were slightly more female than male cases (351/670 [52,4%] versus 319/670, [47,6%], and cases were reported from 17 different health areas. The majority of cases were reported in Mero (205/670 [30,6%]), followed by Kimbangu (115/670 [17,2%]), Kabukungu (105/670 [16,7%]), and Asuku (73/670 [10.9%]). During this period, 7 deaths occurred and 8 out of 14 women who were pregnant had fetal loss. Three healthcare workers acquired mpox infection when caring for patients. In depth case ascertainment showed that 83,4% of patients reported recent visits to bars for (professional) sexual interactions as a likely source of infection. Whole genome sequencing resulted in the generation of 58 genome sequences. Three main clusters characterized by specific mutations were identified and several miniclusters of 2 or more sequences with over two shared mutations. No clear link between sequence cluster, bar or health area was observed. The more recent sequences from Kamanyola were related to the sequences in Kamituga and confirmed to be Clade Ib. However, relatively long branches were observed and one of the sequences clustered with publicly released sequences from travelers in Kenya, Uganda, Sweden and Thailand, indicating more undocumented ongoing spread for cluster A than for the other clusters. Most observed mutations were APOBEC-3 related mutations indicative of ongoing human-to-human transmission. InterpretationThese data suggests that the rapid transmission of monkeypox virus until June 2024 was mostly related to interactions with professional sex workers (PSW) within densely populated health areas. The expanding number of cases and the recent expansion to 29 other nearby health zones of South -Kivu as well as Rwanda, Burundi, Uganda and Kenya stresses the need for cross border surveillance and collaboration. Urgent enhanced response action is needed, including case finding, diagnostic capacity building, health education programmes focussing on sex workers, and possibly vaccination to limit further escalation and stop this outbreak.

BackgroundThe 2019 novel coronavirus disease (COVID-19) outbreak turned into a pandemic, with hundreds of thousands of cases reported globally. The number of cases dramatically increased beginning in early March 2020. AimWe assessed the cumulative change in the incidence and case-fatality rates of COVID-19 at the global, regional, and national levels from January to March 16, 2020, in 154 affected countries and territories globally. MethodsWe collected data of COVID-19 cases using the GitHub repository, which provided real-time surveillance information developed by the Center for Systems Science and Engineering (CSSE), Johns Hopkins University (USA). Information such as confirmed COVID-19 cases, deaths, and recoveries reported across all affected countries was collected from January 22 to March 16, 2020. We estimated the change in the incidence rate, case-fatality rate, and recovery rate from January 22 to February 29 and from March 1 to March 16, 2020. ResultsFrom January 22 to March 16, 2020, globally, the number of incident COVID-19 cases increased by 276.2%, and Europe recorded 65,281 new cases from March 1 to 16, 2020. Overall, the case-fatality rate was 3.92%, with a high COVID-19 fatality rate in Italy (7.7%), Iran (5.7%), China (4.2%) and the United Kingdom (3.6%). The estimated percentage change in COVID-19 cases from March 1 to 16, 2020, was highest in Belgium (105.8/100,000 population), followed by Qatar (439/100,000 population) and Portugal (331/100,000 population). The overall recovery rate of COVID-19 was 43%; China (35.5%) had the highest recovery rate, while the United States of America recorded a recovery rate of 0.3%. ConclusionOverall, all the COVID-19-affected countries showed an upward trend in incidence, with little change in the incidence rate of -0.20% from January to Mid-March. The case-fatality rate was found to be 3.92%, and the recovery rate was observed to be less than half (43%) among COVID-19 patients. Italy, Iran, and Spain had the largest numbers of new cases of COVID-19 from March 1 to 16, 2020.

Since the discovery of the novel coronavirus (SARS-CoV-2), COVID-19 has become a global healthcare and economic crisis. The United States (US) and Europe exhibited wide impacts from the virus with more than six million cases by the time of our analysis. To inhibit spread, stay-at-home orders and other non-pharmaceutical interventions (NPIs) were instituted. Beginning late April 2020, some US states, European, and Asian countries lifted restrictions and started the reopening phases. In this study, the changes of confirmed cases, hospitalizations, and deaths were analyzed after reopening for 11 countries and 40 US states using an interrupted time series analysis. Additionally, the distribution of these categories was further analyzed by age due to the known increased risk in elderly patients. Reopening had varied effects on COVID-19 cases depending on the region. Recent increases in cases did not fully translate into increased deaths. Eight countries had increased cases after reopening while only two countries showed the same trend in deaths. In the US, 30 states had observed increases in cases while only seven observed increased deaths. In addition, we found that states with later reopening dates were more likely to have significant decreases in cases, hospitalizations, and deaths. Furthermore, age distributions through time were analyzed in relation to COVID-19 in the US. Younger age groups typically had an increased share of cases after reopening.

BackgroundLarge-scale population-based seroprevalence studies of SARS-CoV-2 are essential to characterize the cumulative incidence of SARS-CoV-2 infection and to extrapolate the prevalence of presumptive immunity at the population level. ObjectiveThe objective of our survey was to estimate the cumulative population immunity for COVID-19 and to identify individual characteristics associated with a positive serostatus. MethodThis was a clustered cross-sectional study conducted from July 12 to August 20, 2021, in households in the city of Bangui, the capital of the Central African Republic. Information regarding demographic characteristics (age, gender, and place of residence), comorbidities (chronic diseases) was collected. A venous blood sample was obtained for each participant to determine the level of total anti-SARS-CoV-2 antibodies using a WANTAI SARS-CoV-2 Ab ELISA kit. ResultsAll up, 799 participants were surveyed. The average age was 27 years, and 45{middle dot}8% of the respondents were male (sex ratio: 0.8). The overall proportion of respondents with a positive serostatus was 74{middle dot}1%. Participants over 20 years of age were twice as likely to have a positive serostatus, with an OR of 2.{middle dot} {middle dot}2 (95% CI: [1{middle dot}6, 3{middle dot}1]). InterpretationThe results of this survey revealed a high cumulative level of immunity in Bangui, thus indicating a significant degree of spread of SARS-CoV-2 in the population. The public health implications of this high level of immunity to SARS-CoV-2, particularly on its variants burden, remain to be determined. FundingThis study was funded by the French Ministry for Europe and Foreign Affairs through the REPAIR COVID-19-Africa project coordinated by the Pasteur International Network association.

BackgroundThe 2019 coronavirus disease (COVID-19) represents a significant public health threat globally. Here we describe efforts to compare epidemic growth, size and peaking time for countries in Asia, Europe, North America, South America and Australia in the early epidemic phase. MethodsUsing the time series of cases reported from January 20, 2020 to February 13, 2020 and transportation data from December 1, 2019 to January 23, 2020 we have built a novel time-varying growth model to predict the epidemic trend in China. We extended our method, using cases reported from January 26, 2020 - or the date of the earliest case reported, to April 9, 2020 to predict future epidemic trend and size in 41 countries. We estimated the impact of control measures on the epidemic trend. ResultsOur time-varying growth model yielded high concordance in the predicted epidemic size and trend with the observed figures in C hina. Among the other 41 countries, the peak time has been observed in 28 countries before or around April 9, 2020; the peak date and epidemic size were highly consistent with our estimates. We predicted the remaining countries would peak in April or May 2020, except India in July and Pakistan in August. The epidemic trajectory would reach the plateau in May or June for the majority of countries in the current wave. Countries that could emerge to be new epidemic centers are India, Pakistan, Brazil, Mexico, and Russia with a prediction of 105 cases for these countries. The effective reproduction number Rt displayed a downward trend with time across countries, revealing the impact of the intervention remeasures i.e. social distancing. Rt remained the highest in the UK (median 2.62) and the US (median 2.19) in the fourth week after the epidemic onset. ConclusionsNew epidemic centers are expected to continue to emerge across the whole world. Greater challenges such as those in the healthcare system would be faced by developing countries in hotspots. A domestic approach to curb the pandemic must align with joint international efforts to effectively control the spread of COVID-19. Our model promotes a reliable transmissibility characterization and epidemic forecasting using the incidence of cases in the early epidemic phase.

BackgroundSARS-CoV-2 is a novel coronavirus described for the first time in China in December 2019. This virus can cause a disease that ranges in spectrum from asymptomatic to severe respiratory disease with multiorgan failure, and the most severe cases are associated with some comorbidities and patient age. However, there are patients who do not have those risk factors who still develop serious disease. MethodsIn this study, we identified the presence of other respiratory viruses in positive cases of COVID-19 in Mexico to determine if any coinfections were correlated with more severe manifestations of COVID-19. We analysed 103 confirmed cases of COVID-19 using RT-qPCR for the detection of 16 other respiratory viruses. ResultsOf the cases analysed, 14 (13.6%) were cases of coinfection, and 92% of them never required hospitalization, even when comorbidities and advanced age were involved. There werent significant differences between the presence of comorbidities and the mean ages of the groups ConclusionsThese results suggest that coinfection is not related to more severe COVID-19 and that, depending on the virus involved, it could even lead to a better prognosis. We believe that our findings may lay the groundwork for new studies aimed at determining the biological mechanism by which this phenomenon occurs and for proposing corresponding strategies to limit the progression to severe cases of COVID-19. CLINICAL TRIAL REGISTRATIONNot apply

BackgroundThe COVID-19 outbreak in sub-Saharan African countries started after those in Asia, Europe and North America, on 28th February 2020. The susceptibility to infection of populations in that region has been debated. Outbreaks on the scale of those seen elsewhere would pose substantial challenges. There are reasons for concern that transmission may be high and difficult to control, rapidly exceeding capacity to meet the needs for hospitalization and critical care. MethodsWe obtained data on daily new confirmed cases for all 46 countries from the World Health Organization, and used these to model and visualize growth trajectories using an AutoRegressive Integrated Moving Average (ARIMA) model. We then estimated doubling times from growth rates estimated from Poisson regression models, and by back counting from the most recent observation. We also calculated the time from 1st to 50th case, and the time from 5th to 100th case. These indicators were compared with the same summary indicators of growth at the same stage of the outbreak in highly affected European countries. ResultsKenya was the only country with clear evidence of exponential growth. Nineteen countries had either reported no cases, were in the first few days of the outbreak, or had reported fewer than 10 cases over a period of two or more weeks. For the remaining 27 countries we identified four growth patterns: slow linear growth, more rapid linear growth, variable growth patterns over the course of the outbreak, and early signs of possible exponential growth. For those in the last three groups, doubling times ranged from 3 to 4 days, times from 1st to 50th case from 12 to 29 days, and from 5th to 100th case from eight to 15 days. These early indicators are comparable to those in European countries that have gone on to have substantial outbreaks, and time to 50th case was shorter suggesting lesser effectiveness of contact-tracing and quarantine in the early phase. ConclusionThe 46 sub-Saharan African countries, home to over one billion people, are at a tipping point with clear potential for the outbreak to follow a similar course as in HIC in the global north. Radical population-level physical distancing measures may be required, but their impact on poor, disadvantaged and vulnerable people and communities need mitigating. Health systems in the region need urgent technical and material support, with testing, personal protection, and hospital/ critical care.

IntroductionIncreased transmissibility of B.1.17 variant of concern (VOC) in the UK may explain its rapid emergence and global spread. We analysed data from putative household infector - infectee pairs in the Virus Watch Community cohort study to assess the serial interval of COVID-19 and whether this was affected by emergence of the B.1.17 variant. MethodsThe Virus Watch study is an online, prospective, community cohort study following up entire households in England and Wales during the COVID-19 pandemic. Putative household infector-infectee pairs were identified where more than one person in the household had a positive swab matched to an illness episode. Data on whether individual infections were caused by the B.1.1.7 variant were not available. We therefore developed a classification system based on the percentage of cases estimated to be due to B.1.17 in national surveillance data for different English regions and study weeks. ResultsOut of 24,887 illnesses reported, 915 tested positive for SARS-CoV-2 and 186 likely infector-infectee pairs in 186 households amongst 372 individuals were identified. The mean COVID-19 serial interval was 3.18 (95%CI: 2.55 - 3.81) days. There was no significant difference (p=0.267) between the mean serial interval for Variants of Concern (VOC) hotspots (mean = 3.64 days, (95%CI: 2.55 - 4.73)) days and non-VOC hotspots, (mean = 2.72 days, (95%CI: 1.48 - 3.96)). ConclusionsOur estimates of the average serial interval of COVID-19 are broadly similar to estimates from previous studies and we find no evidence that B.1.1.7 is associated with a change in serial intervals. Alternative explanations such as increased viral load, longer period of viral shedding or improved receptor binding may instead explain the increased transmissibility and rapid spread and should undergo further investigation.

The emergence of the SARS-CoV-2 Delta variant of concern (lineage B.1.617.2) in late 2020 resulted in a new wave of infections in many countries across the world, where it often became the dominant lineage in a relatively short amount of time. We here report on a novel genomic surveillance effort in Rwanda in the time period from June to September 2021, leading to 201 SARS-CoV-2 genomes being generated, the majority of which were identified as the Delta variant of concern. We show that in Rwanda, the Delta variant almost completely replaced the previously dominant A.23.1 and B.1.351 (Beta) lineages in a matter of weeks, and led to a tripling of the total number of COVID-19 infections and COVID-19-related fatalities over the course of only three months. We estimate that Delta in Rwanda had an average growth rate advantage of 0.034 (95% CI 0.025-0.045) per day over A.23.1, and of 0.022 (95% CI 0.012-0.032) over B.1.351. Phylogenetic analysis reveals the presence of at least seven local Delta transmission clusters, with two of these clusters occurring close to the border with the Democratic Republic of the Congo, and another cluster close to the border with Tanzania. A smaller Delta cluster of infections also appeared close to the border with Uganda, illustrating the importance of monitoring cross-border traffic to limit the spread between Rwanda and its neighboring countries. We discuss our findings against a background of increased vaccination efforts in Rwanda, and also discuss a number of breakthrough infections identified during our study. Concluding, our study has added an important collection of data to the available genomes for the Eastern Africa region, with the number of Delta infections close to the border with neighboring countries highlighting the need to further strengthen genomic surveillance in the region to obtain a better understanding of the impact of border crossings on lowering the epidemic curve in Rwanda.

ObjectiveTo explain the global between-countries variance in number of deaths per million citizens (nDpm) and case fatality rate (CFR) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. DesignSystematic analysis. Data sourcesWorldometer, European Centre for Disease Prevention and Control, United Nations Main outcome measuresThe explanators of nDpm and CFR were mathematically hypothesised and tested on publicly-available data from 88 countries with linear regression models on May 1st 2020. The derived explanators - age-adjusted infection fatality rate (IFRadj) and case detection rate (CDR) - were estimated for each country based on a SARS-CoV-2 model of China. The accuracy and agreement of the models with observed data was assessed with R2 and Bland-Altman plots, respectively. Sensitivity analyses involved removal of outliers and testing the models at five retrospective and four prospective time points. ResultsGlobally, IFRadj estimates varied between countries, ranging from below 0.2% in the youngest nations, to above 1.3% in Portugal, Greece, Italy, and Japan. The median estimated global CDR of SARS-CoV-2 infections on April 16th 2020 was 12.9%, suggesting that most of the countries have a much higher number of cases than reported. At least 93% and up to 99% of the variance in nDpm was explained by reported prevalence expressed as cases per million citizens (nCpm), IFRadj, and CDR. IFRadj and CDR accounted for up to 97% of the variance in CFR, but this model was less reliable than the nDpm model, being sensitive to outliers (R2 as low as 67.5%). ConclusionsThe current differences in SARS-CoV-2 mortality between countries are driven mainly by reported prevalence of infections, age distribution, and CDR. The nDpm might be a more stable estimate than CFR in comparing mortality burden between countries.

The COVID-19 pandemic exposed gaps in global emerging infectious disease preparedness, particularly for countries with high levels of economic migration, such as Nepal. The Epidemic Intelligence project analysed SARS-CoV-2 genomic and epidemiological data from 2,046 COVID-19 patients across three Nepali regions to study viral dynamics, phylogeography, and long COVID. Participants were interviewed at diagnosis, and at 3-, 6-, and 12-months post-infection. Long COVID was explored using three standardised definitions from the literature, and logistic regression was used to identify risk factors, stratified by SARS-CoV-2 variant. Over 40% of participants reported long COVID at 3 months defined by symptoms [≥]12 weeks and the presence of at least one symptom from the Wellcome Trust Longitudinal Population Study questionnaire. 10.6% (n=211/1992) of participants reported at least two of the symptom clusters from the WHO Delphi consensus definition at 3 months, and 0.7% (n=14/1992) reported all three symptom clusters. The prevalence of long COVID was higher among people infected with the Delta variant than the Omicron variant across all definitions and time points. Among Delta cases, female sex, chronic comorbidity, and full vaccination were associated with long COVID; whereas for people infected with Omicron, only age between 40-54 was a risk factor. Phylogeographic analyses revealed the epidemiological importance of Nepals porous border with India and the changing role of migration in viral spread as the pandemic evolved. Our findings also highlight the burden of post-COVID syndrome in low-resource settings and the need for further research to mitigate the financial and clinical impact. Integration of pathogen genomic analysis with clinical and epidemiological data can guide public health responses during the emergence of novel infectious diseases.

In sub-Saharan Africa, reported COVID-19 numbers have been lower than anticipated, even when considering populations younger age. The extent to which risk factors, established in industrialised countries, impact the risk of infection and of disease in populations in sub-Saharan Africa, remains unclear. We estimated the incidence of mild and moderate COVID-19 in urban Mozambique and analysed factors associated with infection and disease in a population-based surveillance study. During December 2020-March 2022, households of a population cohort in Polana Canico, Maputo, Mozambique, were contacted biweekly. Residents reporting any respiratory sign, anosmia, or ageusia, were asked to self-administer a nasal swab, for SARS-CoV-2 PCR testing. Of a subset of 1400 participants, dried blood spots were repeatedly collected three-monthly from finger pricks at home. Antibodies against SARS-CoV-2 spike glycoprotein and nucleocapsid protein were detected using an in-house developed multiplex antibody assay. We estimated the incidence of respiratory illness and COVID-19, and SARS-CoV-2 seroprevalence. We used Cox regression models, adjusting for age and sex, to identify factors associated with first symptomatic COVID-19 and with SARS-CoV-2 sero-conversion in the first six months. During 11925 household visits in 1561 households, covering 6049 participants (median 21 years, 54.8% female, 7.3% disclosed HIV positive), 1895.9 person-years were followed up. Per 1000 person-years, 364.5 (95%CI 352.8-376.1) respiratory illness episodes of which 72.2 (95%CI 60.6-83.9) COVID-19 confirmed, were reported. Of 1412 participants, 2185 blood samples were tested (median 30.6 years, 55.2% female). Sero-prevalence rose from 4.8% (95%CI 1.1-8.6%) in December 2020 to 34.7% (95%CI 20.2-49.3%) in June 2021, when 3.0% were vaccinated. Increasing age (strong gradient in hazard ratio, HR, up to 15.70 in [≥]70 year olds, 95%CI 3.74-65.97), leukaemia, chronic lung disease, hypertension, and overweight increased risk of COVID-19. We found no increased risk of COVID-19 in people with HIV or tuberculosis. Risk of COVID-19 was lower among residents in the lowest socio-economic quintile (HR 0.16, 95%CI 0.04-0.64), with no or limited handwashing facilities, and who shared bedrooms (HR 0.42, 95%CI 0.25-0.72). Older age also increased the risk of SARS-CoV-2 seroconversion (HR 1.57 in 60-69 year olds, 95%CI 1.03-2.39). We found no associations between SARS-CoV-2 infection risk and socio-economic, behavioural factors and comorbidities. Active surveillance in an urban population cohort confirmed frequent COVID-19 underreporting, yet indicated that the large majority of cases were mild and non-febrile. In contrast to industrialised countries, deprivation did not increase the risk of infection nor disease.

The novel coronavirus disease 2019 (COVID-19) has been the biggest public health problem of the present world. As the number of people suffering from the pandemic is rising, it is likely to claim more life and worsen the global health and economy. Nepal, one of the developing countries in the south Asia has been strongly influenced by the pandemic and struggling to contain it with multiple interventions, however, spatiotemporal dynamics of the epidemic and its linkage with various intervention strategies has not been studied yet. Here, we employed the prospective spatial-temporal analysis with SaTScan assessing dynamics of the COVID-19 cases from 23 January to 31 August 2020 at district level in Nepal. The results revealed that COVID-19 dynamics in the early stage of transmission was slower and confined in certain districts. However, from the third week of April, transmission spread rapidly across districts of Province No. 2 and Sudoorpaschim Province, primarily introduced by Nepalese citizens returning from India. Despite nationwide lockdown, nine statistically significant active and emerging clusters were detected between 23 January and 21 July 2020, whereas ten emerging clusters were observed for extended period to 31 August. The population density and population inflow from India crossing the sealed border had significant effects on the elevated risk of the epidemic. The capital city Kathmandu has become the highest-risk active cluster since August when travel restriction has been suspended. Movement restriction appears to be the most effective non-pharmaceutical intervention against the COVID-19 for resource-scarce countries with limited health care facilities. Our findings could be valuable to the health authorities within Nepal and beyond to better allocate resources and improve interventions on the pandemic for containing it efficiently.

BackgroundEarly in the COVID-19 pandemic, it was noted that males seemed to be more affected than females. We examined the magnitude and consistency of the sex differences in age-specific case-fatality rates (CFRs) in six countries. MethodsData on the cases and deaths from COVID-19, by sex and age group, were extracted from the published reports from Denmark, England, Israel, Italy, Spain, and the United States. Age-specific CFRs were computed for males and females separately. The ratio of the male to female CFRs were computed and meta-analytic methods were used to obtained pooled estimates of the male to female ratio of the CFRs over the six countries, for seven age-groups. FindingsThe CFRs were consistently higher in males at all ages. The differences were greater in the younger age groups. The pooled M:F CFR ratios were 2.53, 2.92, 2.57, 1.83, 1.57, 1.58 and 1.48 for ages 0-39, 40-49, 50-59, 60-69, 70-79, 80-89 and 90+. There was remarkable consistency between countries in the magnitude of the M:F CFRs, in each age group. In meta-regression, age group explained almost all the heterogeneity in the CFR ratios. ConclusionsThe sex differences in the CFRs are intriguing and are compatible with the male dominance in the incidence rates of many infectious diseases. For COVID-19, factors such as sex differences in the prevalence of underlying diseases may play a part in the CFR differences. However, the greater severity of the disease in males, particularly at younger ages, may be part of the disease mechanism and should be explored further. FundingNo funding was provided for this study. The authors declare no conflict of interests