International Journal of Infectious Diseases

A population-representative serological study was conducted in all districts of the state of Tamil Nadu (population 72 million), India, in October-November 2020. State-level seroprevalence was 31.6%. However, this masks substantial variation across the state. Seroprevalence ranged from just 11.1% in The Nilgris to 51.0% in Perambalur district. Seroprevalence in urban areas (36.9%) was higher than in rural areas (26.9%). Females (30.8%) had similar seroprevalence to males (30.3%). However, working age populations (age 40-49: 31.6%) have significantly higher seroprevalence than the youth (age 18-29: 30.7%) or elderly (age 70+: 25.8%). Estimated seroprevalence implies that at least 22.6 million persons were infected by the end of November, roughly 36 times the number of confirmed cases. Estimated seroprevalence implies an infection fatality rate of 0.052%.

Four rounds of serological surveys were conducted, spanning two COVID waves (October 2020 and April-May 2021), in Tamil Nadu (population 72 million) state in India. Each round included representative populations in each district of the state, totaling [≥]20,000 persons per round. State-level seroprevalence was 31.5% in round 1 (October-November 2020), after Indias first COVID wave. Seroprevalence fell to 22.9% in 2 (April 2021), consistent with waning of antibodies from natural infection. Seroprevalence rose to 67.1% by round 3 (June-July 2021), reflecting infections from the Delta-variant induced second COVID wave. Seroprevalence rose to 93.1% by round 4 (December 2021-January 2022), reflecting higher vaccination rates. Antibodies also appear to wane after vaccination. Seroprevalence in urban areas was higher than in rural areas, but the gap shrunk over time (35.7 v. 25.7% in round 1, 89.8% v. 91.4% in round 4) as the epidemic spread even in low-density rural areas. Article Summary LineAntibodies waned after Indias first COVID wave and both vaccination and infection contributed its roughly 90% seroprevalence after its second wave.

The aim of this piece is to provide estimates of the infection fatality rate (IFR) of COVID-19 in Mumbai during 2020, namely the fraction of SARS-CoV-2 infections which resulted in death. Estimates are presented for slums and nonslum areas, and for the city as a whole. These are based largely on the citys official COVID-19 fatality data, seroprevalence data, and all-cause mortality data. Using recorded COVID-19 fatalities in the numerator, we obtain IFR estimates of 0.13%-0.17%. On the other hand, using excess deaths we obtain IFR estimates of 0.28%-0.40%. The estimates based on excess deaths are broadly consistent with the citys age structure, and meta-analyses of COVID-19 age-stratified IFR. If excess deaths were largely from COVID-19, then only around half of COVID-19 deaths were officially recorded in the city. The analysis indicates that levels of excess mortality in excess deaths per 1000 population were similar in the citys slums and nonslum areas. On the other hand the estimated ratio of excess deaths to official COVID-19 deaths in the slums was much higher than in nonslum areas, suggesting much weaker COVID-19 death reporting from the slums.

SARS-CoV-2 emerged in Wuhan, China, in December 2019. Starting in January 2020, over a period of several months, the initial virus (Wuhan-Hu-1/2019; Wu et al. 2020) diverged in a descendant strain carrying D614G amino acid mutation in spike protein. By summer 2020 this novel coronavirus (nCoV) became the most dominant form of the virus circulating worldwide and raised serious international concern. Currently (April 2021), there are 3598 subsequent PANGO branched lineages recognized that carry numerous mutations. To date, the most emerging lineages of SARS-CoV-2 worldwide include B.1.1.7 lineage with a frequency of 48% followed by several dozens of others with frequencies 7.5% or less, such as B.1.351, B.1.1.28, B.1.2, B.1.1.519, P.1, R.1, etc. (www.nextrain.org, Centers for Disease Control and Prevention; CDC 2020 www.cdc.gov). In this study, we monitored the spreading of B.1.1.7 lineage from the early phase of its appearance until it became predominant in the South-Moravian region of the Czech Republic. We measured significantly associated clinical marker (Ct; cycle threshold) correlating with viral load in B.1.1.7 lineage. Interestingly, we found that the spreading of B.1.1.7 strain was associated with a shift in patients average age, as compared to the former predominant lineage. Finally, we calculated the impact of the B.1.1.7 lineage on hospitalization and case fatality of the patients on the intensive care unit in the central South-Moravian faculty hospital.

Coronavirus disease 2019 has led to more than three million cases globally. Since the first family cluster of COVID-19 cases identified in Shenzhen in early January, most of the local transmission occurred within household contacts. Identifying the factors associated with household transmission is of great importance to guide preventive measures.

BackgroundBy mid-September of 2020, the number of daily new infections in India crossed 95, 000. We aimed to characterize the spatio-temporal shifts in the disease burden as the infections rose during the first wave of COVID-19. MethodsWe gathered the publicly available district-level (equivalent of counties) granular data for the 15 April to 31 August 2020 period. We used the epidemiological data from 186 districts with the highest case burden as of August 31, 559, 566 active cases and 2, 715, 656 cumulative infections, and the governing epidemic parameters were estimated by fitting it to a susceptible-asymptomatic-infected-recovered-dead (SAIRD) model. The space-time trends in the case burden and epidemic parameters were analyzed. When the physical proximity of the districts did not explain the spreading patterns, we developed a metric for accessibility of the districts via air and train travel. The districts were categorized as large metro, metro, urban and sub-urban and the spatial shifts in case burden were analyzed. ResultsThe center of the burden of the current-active infections which on May 15 was in the large metro districts with easy international access shifted continuously and smoothly towards districts which could be accessed by domestic airports and by trains. A linear trend-analysis showed a continuous improvement in the governing epidemic parameters consistently across the four categories of districts. The reproduction numbers improved from 1.77 {+/-} 0.58 on May 15 to 1.07 {+/-} 0.13 on August 31 in large metro districts (p-Value of trend 0.0001053); and from 1.58 {+/-} 0.39 on May 15 to 0.94 {+/-} 0.11 on August 31 in sub-urban districts (p-Value of trend 0.0067). The recovery rate per infected person per day improved from 0.0581 {+/-} 0.009 on May 15 to 0.091 {+/-} 0.010 on August 31 in large metro districts (p-Value of trend 0.26 x 10-12); and from 0.059 {+/-} 0.011 on May 15 to 0.100 {+/-} 0.010 on August 31 in sub-urban districts (p-Value of trend 0.12 x 10-16). The death rate of symptomatic individuals which includes the case-fatality-rate as well as the time from symptoms to death, consistently decreased from 0.0025 {+/-} 0.0014 on May 15 to 0.0013 {+/-} 0.0003 on August 31 in large metro districts (p-Value of trend 0.0010); and from 0.0018 {+/-} 0.0008 on May 15 to 0.0014 {+/-} 0.0003 on August 31 in sub-urban districts (p-Value of trend 0.2789). ConclusionsAs the daily infections continued to rise at a national level, the "center" of the pandemic-burden shifted smoothly and predictably towards smaller sized districts in a clear hierarchical fashion of accessibility from an international travel perspective. This observed trend was meant to serve as an alert to re-organize healthcare resources towards remote districts. The geographical spreading patterns continue to be relevant as the second wave of infections began in March 2021 with a center in the mid-range districts. FundingNone

The estimated mortality rate of the SARS-CoV-2 pandemic varied greatly around the world with multiple countries in East, Central, and West Africa having significantly lower rates of COVID-19 related fatalities than many resource-rich nations with significantly earlier wide-spread access to life-saving vaccines. One possible reason for this lower mortality could be the presence of pre-existing cross-reactive immunological responses in these areas of the world. To explore this hypothesis, stored peripheral blood mononuclear cells (PBMC) from Ugandans collected from 2015-2017 prior to the COVID-19 pandemic (n=29) and from hospitalized Ugandan COVID-19 patients (n=3) were examined using flow-cytometry for the presence of pre-existing SARS-CoV-2 cross-reactive CD4+ and CD8+ T-cell populations using four T-cell epitope mega pools. Of pre-pandemic participants, 89.7% (26/29) had either CD4+ or CD8+, or both, SARS-CoV-2 specific T-cell responses. Specifically, CD4+ T-cell reactivity (72.4%) and CD8+ T-cell reactivity (65.5%) were relatively similar, and 13 participants (44.8%) had both types of cross-reactive types of T-cells present. There were no significant differences in response by sex in the population. The rates of cross-reactive T-cell populations in these Ugandans is higher than previous estimates from resource-rich countries like the United States (20-50% reactivity). It is unclear what role, if any, this cross-reactivity played in decreasing COVID-19 related mortality in Uganda and other African countries, but does suggest that a better understanding of global pre-existing immunological cross-reactivity could be an informative data of epidemiological intelligence moving forward.

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.

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.

We analyzed transmission of coronavirus disease 2019 in South Korea. We estimated that non-pharamaceutical measures reduced the immediate transmissibility by maximum of 34% for coronavirus disease 2019. Continuous efforts are needed for monitoring the transmissibility to optimize epidemic control.

Seroepidemiology and genomics are valuable tools to investigate the transmission of COVID-19. We utilized qRT-PCR, serum antibody immunoassays, and whole genome sequencing to examine the spread of SARS-CoV-2 infections in North East (NE) region of India during the first and second pandemic waves (June 2020 to September 2021). qRT-PCR analysis was performed on a selected population from NE India during June 2020 to July 2021, and metadata were collected for the region. Seroprevalence and neutralizing antibody immunoassay were studied on selected individuals (n=2026) at three time points (August 2020, February 2021 and June 2021), as well as in a cohort (n=35) for a year (August 2020 to August 2021). SARS-CoV-2 genomes of 914 qRT-PCR positive samples (June 2020 to September 2021) were sequenced and assembled, and those obtained from the sequence databases were analyzed. Test positivity rates in first and second waves were 6.34% and 6.64% in the state of Assam, respectively, and a similar pattern was observed in other NE states. Seropositivity in August 2020, February 2021, and June 2021 were 10.63%, 40.3% and 46.33% respectively, and neutralizing antibody prevalence were 90.91%, 52.14%, and 69.30% respectively. The cohort group showed the presence of stable neutralizing antibody throughout the year. Normal variants dominated the first wave, while the variant of concerns (VOCs) B.1.617.2 and AY-sublineages dominated the second wave, and identified mostly among vaccinated individuals. All eight states of NE India reported numerous incidences of SARS-CoV-2 VOCs, especially B.1.617.2 and AY sublineages, and their prevalence co-related well with high TPR and seropositivity rate in the region. High infection and seroprevalence of COVID-19 in NE India during the second wave was associated with the emergence of VOCs. Natural infection prior to vaccination provided higher neutralizing activity than vaccination alone.

Since the first case of Novel Coronavirus (2019-nCov) was identified in December 2019 in Wuhan City, China, the number of cases continues to grow across China and multiple cases have been exported to other countries. The cumulative number of reported deaths is at 637 as of February 7, 2020. Here we statistically estimated the time-delay adjusted death risk for Wuhan as well as for China excluding Wuhan to interpret the current severity of the epidemic in China. We found that the latest estimates of the death risk in Wuhan could be as high as 20% in the epicenter of the epidemic whereas we estimate it [~]1% in the relatively mildly-affected areas. Because the elevated death risk estimates are likely associated with a breakdown of the medical/health system, enhanced public health interventions including social distancing and movement restrictions should be effectively implemented to bring the epidemic under control.

Although the vast majority of confirmed cases of COVID-19 are in low- and middle-income countries, there are relatively few published studies on the epidemiology of SARS-CoV-2 in these countries. The few there are focus on disease prevalence in urban areas. We conducted state-wide surveillance for COVID-19, in both rural and urban areas of Karnataka between June 15-August 29, 2020. We tested for both viral RNA and antibodies targeting the receptor binding domain (RBD). Adjusted seroprevalence across Karnataka was 46.7% (95% CI: 43.3-50.0), including 44.1% (95% CI: 40.0-48.2) in rural and 53.8% (95% CI: 48.4-59.2) in urban areas. The proportion of those testing positive on RT-PCR, ranged from 1.5 to 7.7% in rural areas and 4.0 to 10.5% in urban areas, suggesting a rapidly growing epidemic. The relatively high prevalence in rural areas is consistent with the higher level of mobility measured in rural areas, perhaps because of agricultural activity. Overall seroprevalence in the state implies that by August at least 31.5 million residents had been infected by August, nearly an order of magnitude larger than confirmed cases.

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.

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.

To understand the effect of nationwide lockdown on transmissibilty of SARS-CoV-2 in India, time varying reproduction number during the first weeks of April, 2020 was estimated. The time varying reproduction number was estimated using EpiEstim package in R programming language. The reproduction number has come down significantly during the lockdown period both at national level and in most states but it wasnt reduced to less than 1. This calls for urgent need for more effective control measures in addition to lockdown to stop the epidemic spread of the virus.

ObjectiveThis population-based study aimed to evaluate the seroprevalence of antibodies to SARS-CoV-2 in Duhok City, Kurdistan Region of Iraq. MethodsWe analyzed the national COVID-19 database that contains data regarding COVID-19 testing, management, and clinical outcomes in Duhok. For this study, different subdistricts within each district of Duhok were considered distinct clusters. Blood samples were collected from and questionnaires were administered to eligible and consenting participants who were members of different families from the subdistricts. Immunoassays were conducted to detect antibodies against SARS-CoV-2, and the associations between certain variables were investigated. ResultsThe average number cases of COVID-19 before November 2020 was 23141 {+/-} 4364, which was significantly higher than the average number of cases between November 2020 and February 2021 (3737 {+/-} 2634; P = 0.001). A total of 743 individuals agreed to participate and were enrolled in the study. Among the participants, 465/743 (62.58%) were found to have antibodies against severe acute respiratory syndrome coronavirus 2. Among the participants with antibodies, 262/465 (56.34%) denied having any history of COVID-19-related symptoms. The most common symptom was fever (81.77%), followed by myalgia (81.28%). We found that antibody levels increased steadily with age (Pearson correlation coefficient = 0.117; P = 0.012). A significant association was found between antibody levels and the presence of symptoms (P = 0.023; odds ratio = 1.0023; 95% confidence interval = 1.0002-1.0061). ConclusionsA significant reduction in the number of COVID-19 cases was observed. This might be due to the high prevalence of SARS-CoV-2 antibodies in Duhok. However, infection-prevention measures should be followed as it remains unclear whether acquired immunity is protective against reinfection. It expected that the infection rates during the next wave will not be as high as the first wave due to the high infection rate in the society.

The Bacillus Calmette-Guerin (BCG) vaccine provides protection against tuberculosis (TB), and is proposed to provide protection to non-TB infectious diseases. The COVID-19 outbreak results from infection with the novel coronavirus SARS-CoV-2 (CoV-2) and was declared a pandemic on March 11th, 2020. We queried whether the BCG vaccine offers protection against CoV-2 infection. We observed that countries with a current universal BCG vaccination policy have a significantly lower COVID-19 incidence than countries which never had a universal BCG policy or had one in the past. However, population density, median age, TB incidence, urban population, and, most significantly, CoV-2 testing rate, were also connected with BCG policy and could potentially confound the analysis. By limiting the analysis to countries with high CoV-2 testing rates, defined as greater than 2,500 tests per million inhabitants, these parameters were no longer statistically associated with BCG policy. When analyzing only countries with high testing rates, there was no longer a significant association between the number of COVID-19 cases per million inhabitants and the BCG vaccination policy. Although preliminary, our analyses indicate that the BCG vaccination may not offer protection against CoV-2 infection. While reporting biases may confound our observations, our findings support exercising caution in determining potential correlation between BCG vaccination and COVID-19 incidence, in part due significantly lower rates of CoV-2 testing per million inhabitants in countries with current universal BCG vaccination policy.

ObjectivesTo assess seroprevalence of anti-SARS-CoV-2 antibodies in a densely populated urban Indian settings and its implications for disease trends and protective immunity. DesignCross-sectional sero-epidemiological survey linked with administrative reporting of COVID-19 testing data. SettingsPune city in western India Main outcome measurePrevalence of anti-SARS-CoV-2 spike protein antibodies were estimated and along with correlates of virus neutralisation and other immune and inflammatory markers. ResultsSeropositivity was extensive (51{middle dot}3%; 95%CI 39{middle dot}9 to 62{middle dot}4) but varied widely in the five localities tested, ranging from 35{middle dot}8% to 66{middle dot}4%. Seropositivity was higher in crowded living conditions in the slums (OR 1{middle dot}91), and was lower in those 65 years or older (OR 0{middle dot}59). The infection-fatality ratio was estimated at 0.21%. Post survey, COVID-19 incidence was lower in areas noted to have higher seroprevalence. Substantial virus-neutralising activity was observed in seropositive individuals, but with considerable heterogeneity in the immune response and possible age-dependent diversity in the antibody repertoire. ConclusionDespite crowded living conditions having facilitated widespread transmission, the variability in seroprevalence in localities that are in geographical proximity indicates a heterogenous spread of infection. Declining infection rates in areas with high seropositivity suggest population-level protection. It is also supported by substantial neutralising activity in asymptomatically infected individuals. This is the first report of a significantly high proportion of protective immune response among asymptomatic individuals in the population. The heterogeneity in antibody levels and neutralisation capacity indicates the existence of immunological sub-groups of functional interest. Trial registrationRegistered with the Clinical Trials Registry of India (CTRI/2020/07/026509)

Japan has reported a small number of COVID-19 cases relative to other countries. Because not all infected people receive diagnostic tests for COVID-19, the reported number of COVID-19 cases must be lower than the actual number of infections. Assessments of the presence of antibodies against the spike protein of SARS-CoV-2 can retrospectively determine the history of natural infection and vaccination. In this study, we assessed SARS-CoV-2 seroprevalence by analyzing over 60,000 samples collected in Japan from February 2020 to March 2022. The results showed that about 5% of the Japanese population had been infected with the virus by January 2021. The seroprevalence increased with the administration of vaccinations to adults; however, among the elderly, it was not as high as the vaccination rate, probably due to poor immune responses to the vaccines and waning immunity. The infection was spread during the epidemic waves caused by the SARS-CoV-2 Delta and Omicron variants among children who were not eligible for vaccination. Nevertheless, their seroprevalence was as low as 10% as of March 2022. Our study underscores the low incidence of SARS-CoV-2 infection in Japan and the effects of vaccination on immunity at the population level.