ABSTRACT
Hepatitis E virus (HEV) is a major cause of acute jaundice in South Asia. Gaps in our understanding of transmission are driven by non-specific symptoms and scarcity of diagnostics, impeding rational control strategies. In this context, serological data can provide important proxy measures of infection. We enrolled a population-representative serological cohort of 2,337 individuals in Sitakunda, Bangladesh. We estimated the annual risks of HEV infection and seroreversion both using serostatus changes between paired serum samples collected 9 months apart, and by fitting catalytic models to the age-stratified cross-sectional seroprevalence. At baseline, 15% (95 CI: 14-17%) of people were seropositive, with seroprevalence highest in the relatively urban south. During the study, 27 individuals seroreverted (annual seroreversion risk: 15%, 95 CI: 10-21%), and 38 seroconverted (annual infection risk: 3%, 95CI: 2-5%). Relying on cross-sectional seroprevalence data alone, and ignoring seroreversion, underestimated the annual infection risk five-fold (0.6%, 95 CrI: 0.5-0.6%). When we accounted for the observed seroreversion in a reversible catalytic model, infection risk was more consistent with measured seroincidence. Our results quantify HEV infection risk in Sitakunda and highlight the importance of accounting for seroreversion when estimating infection incidence from cross-sectional seroprevalence data.
Subject(s)
Hepatitis E virus , Hepatitis E , Humans , Bangladesh/epidemiology , Seroepidemiologic Studies , Cross-Sectional Studies , Hepatitis AntibodiesABSTRACT
BACKGROUND: Understanding the drivers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is crucial for control policies, but evidence of transmission rates in different settings remains limited. METHODS: We conducted a systematic review to estimate secondary attack rates (SARs) and observed reproduction numbers (Robs) in different settings exploring differences by age, symptom status, and duration of exposure. To account for additional study heterogeneity, we employed a beta-binomial model to pool SARs across studies and a negative-binomial model to estimate Robs. RESULTS: Households showed the highest transmission rates, with a pooled SAR of 21.1% (95% confidence interval [CI]:17.4-24.8). SARs were significantly higher where the duration of household exposure exceeded 5 days compared with exposure of ≤5 days. SARs related to contacts at social events with family and friends were higher than those for low-risk casual contacts (5.9% vs 1.2%). Estimates of SARs and Robs for asymptomatic index cases were approximately one-seventh, and for presymptomatic two-thirds of those for symptomatic index cases. We found some evidence for reduced transmission potential both from and to individuals younger than 20 years of age in the household context, which is more limited when examining all settings. CONCLUSIONS: Our results suggest that exposure in settings with familiar contacts increases SARS-CoV-2 transmission potential. Additionally, the differences observed in transmissibility by index case symptom status and duration of exposure have important implications for control strategies, such as contact tracing, testing, and rapid isolation of cases. There were limited data to explore transmission patterns in workplaces, schools, and care homes, highlighting the need for further research in such settings.
Subject(s)
COVID-19 , SARS-CoV-2 , Contact Tracing , Family Characteristics , Humans , IncidenceABSTRACT
In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27-77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49-91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12-48%]) post-relaxation. In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.
Subject(s)
COVID-19/transmission , Communicable Disease Control/methods , Pandemics/prevention & control , SARS-CoV-2/isolation & purification , Algorithms , COVID-19/epidemiology , COVID-19/virology , Communicable Disease Control/statistics & numerical data , Global Health , Humans , Models, Theoretical , Physical Distancing , Quarantine/methods , SARS-CoV-2/physiologySubject(s)
Basic Reproduction Number , COVID-19/transmission , Brazil/epidemiology , COVID-19/epidemiology , Humans , Pandemics , SARS-CoV-2 , Time FactorsABSTRACT
OBJECTIVES: In this data collation study, we aimed to provide a comprehensive database describing the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19) throughout the main provinces in China. METHODS: From mid-January to March 2020, we extracted publicly available data regarding the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted descriptive analyses of the epidemic in the six most-affected provinces. RESULTS: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends differed among provinces. Compared with Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as the local transmission of COVID-19 declined, switching the focus of measures to the testing and quarantine of inbound travellers may have helped to sustain the control of the epidemic. CONCLUSIONS: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database containing these indicators and information regarding control measures is a useful resource for further research and policy planning in response to the COVID-19 epidemic.
Subject(s)
COVID-19/epidemiology , SARS-CoV-2 , COVID-19/prevention & control , China/epidemiology , Contact Tracing , Databases, Factual , HumansABSTRACT
BACKGROUND: After experiencing a sharp growth in COVID-19 cases early in the pandemic, South Korea rapidly controlled transmission while implementing less stringent national social distancing measures than countries in Europe and the USA. This has led to substantial interest in their "test, trace, isolate" strategy. However, it is important to understand the epidemiological peculiarities of South Korea's outbreak and characterise their response before attempting to emulate these measures elsewhere. METHODS: We systematically extracted numbers of suspected cases tested, PCR-confirmed cases, deaths, isolated confirmed cases, and numbers of confirmed cases with an identified epidemiological link from publicly available data. We estimated the time-varying reproduction number, Rt, using an established Bayesian framework, and reviewed the package of interventions implemented by South Korea using our extracted data, plus published literature and government sources. RESULTS: We estimated that after the initial rapid growth in cases, Rt dropped below one in early April before increasing to a maximum of 1.94 (95%CrI, 1.64-2.27) in May following outbreaks in Seoul Metropolitan Region. By mid-June, Rt was back below one where it remained until the end of our study (July 13th). Despite less stringent "lockdown" measures, strong social distancing measures were implemented in high-incidence areas and studies measured a considerable national decrease in movement in late February. Testing the capacity was swiftly increased, and protocols were in place to isolate suspected and confirmed cases quickly; however, we could not estimate the delay to isolation using our data. Accounting for just 10% of cases, individual case-based contact tracing picked up a relatively minor proportion of total cases, with cluster investigations accounting for 66%. CONCLUSIONS: Whilst early adoption of testing and contact tracing is likely to be important for South Korea's successful outbreak control, other factors including regional implementation of strong social distancing measures likely also contributed. The high volume of testing and the low number of deaths suggest that South Korea experienced a small epidemic relative to other countries. Caution is needed in attempting to replicate the South Korean response in populations with larger more geographically widespread epidemics where finding, testing, and isolating cases that are linked to clusters may be more difficult.
Subject(s)
Betacoronavirus , Contact Tracing/methods , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Quarantine/methods , Bayes Theorem , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Contact Tracing/trends , Coronavirus Infections/diagnosis , Disease Outbreaks/prevention & control , Humans , Pneumonia, Viral/diagnosis , Quarantine/trends , Republic of Korea/epidemiology , SARS-CoV-2ABSTRACT
Background: The COVID-19 epidemic was declared a Global Pandemic by WHO on 11 March 2020. By 24 March 2020, over 440,000 cases and almost 20,000 deaths had been reported worldwide. In response to the fast-growing epidemic, which began in the Chinese city of Wuhan, Hubei, China imposed strict social distancing in Wuhan on 23 January 2020 followed closely by similar measures in other provinces. These interventions have impacted economic productivity in China, and the ability of the Chinese economy to resume without restarting the epidemic was not clear. Methods: Using daily reported cases from mainland China and Hong Kong SAR, we estimated transmissibility over time and compared it to daily within-city movement, as a proxy for economic activity. Results: Initially, within-city movement and transmission were very strongly correlated in the five mainland provinces most affected by the epidemic and Beijing. However, that correlation decreased rapidly after the initial sharp fall in transmissibility. In general, towards the end of the study period, the correlation was no longer apparent, despite substantial increases in within-city movement. A similar analysis for Hong Kong shows that intermediate levels of local activity were maintained while avoiding a large outbreak. At the very end of the study period, when China began to experience the re-introduction of a small number of cases from Europe and the United States, there is an apparent up-tick in transmission. Conclusions: Although these results do not preclude future substantial increases in incidence, they suggest that after very intense social distancing (which resulted in containment), China successfully exited its lockdown to some degree. Elsewhere, movement data are being used as proxies for economic activity to assess the impact of interventions. The results presented here illustrate how the eventual decorrelation between transmission and movement is likely a key feature of successful COVID-19 exit strategies.
Subject(s)
Air Travel/statistics & numerical data , COVID-19 , Communicable Disease Control/methods , Epidemiological Monitoring , SARS-CoV-2/isolation & purification , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , COVID-19 Nucleic Acid Testing/methods , COVID-19 Nucleic Acid Testing/statistics & numerical data , China/epidemiology , Epidemiologic Measurements , Humans , International Health Regulations/organization & administration , Prevalence , Travel Medicine/methods , Travel Medicine/trends , United Kingdom/epidemiologyABSTRACT
BACKGROUND: COVID-19 has the potential to cause substantial disruptions to health services, due to cases overburdening the health system or response measures limiting usual programmatic activities. We aimed to quantify the extent to which disruptions to services for HIV, tuberculosis, and malaria in low-income and middle-income countries with high burdens of these diseases could lead to additional loss of life over the next 5 years. METHODS: Assuming a basic reproduction number of 3·0, we constructed four scenarios for possible responses to the COVID-19 pandemic: no action, mitigation for 6 months, suppression for 2 months, or suppression for 1 year. We used established transmission models of HIV, tuberculosis, and malaria to estimate the additional impact on health that could be caused in selected settings, either due to COVID-19 interventions limiting activities, or due to the high demand on the health system due to the COVID-19 pandemic. FINDINGS: In high-burden settings, deaths due to HIV, tuberculosis, and malaria over 5 years could increase by up to 10%, 20%, and 36%, respectively, compared with if there was no COVID-19 pandemic. The greatest impact on HIV was estimated to be from interruption to antiretroviral therapy, which could occur during a period of high health system demand. For tuberculosis, the greatest impact would be from reductions in timely diagnosis and treatment of new cases, which could result from any prolonged period of COVID-19 suppression interventions. The greatest impact on malaria burden could be as a result of interruption of planned net campaigns. These disruptions could lead to a loss of life-years over 5 years that is of the same order of magnitude as the direct impact from COVID-19 in places with a high burden of malaria and large HIV and tuberculosis epidemics. INTERPRETATION: Maintaining the most critical prevention activities and health-care services for HIV, tuberculosis, and malaria could substantially reduce the overall impact of the COVID-19 pandemic. FUNDING: Bill & Melinda Gates Foundation, Wellcome Trust, UK Department for International Development, and Medical Research Council.
Subject(s)
Coronavirus Infections/epidemiology , Developing Countries , HIV Infections/prevention & control , Health Services Accessibility , Malaria/prevention & control , Pandemics , Pneumonia, Viral/epidemiology , Tuberculosis/prevention & control , COVID-19 , HIV Infections/epidemiology , HIV Infections/mortality , Humans , Malaria/epidemiology , Malaria/mortality , Models, Theoretical , Tuberculosis/epidemiology , Tuberculosis/mortalityABSTRACT
The ongoing coronavirus disease 2019 (COVID-19) pandemic poses a severe threat to public health worldwide. We combine data on demography, contact patterns, disease severity, and health care capacity and quality to understand its impact and inform strategies for its control. Younger populations in lower-income countries may reduce overall risk, but limited health system capacity coupled with closer intergenerational contact largely negates this benefit. Mitigation strategies that slow but do not interrupt transmission will still lead to COVID-19 epidemics rapidly overwhelming health systems, with substantial excess deaths in lower-income countries resulting from the poorer health care available. Of countries that have undertaken suppression to date, lower-income countries have acted earlier. However, this will need to be maintained or triggered more frequently in these settings to keep below available health capacity, with associated detrimental consequences for the wider health, well-being, and economies of these countries.
Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Developing Countries , Global Health , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Poverty , COVID-19 , Coronavirus Infections/transmission , Humans , Patient Acceptance of Health Care , Pneumonia, Viral/transmission , Public HealthABSTRACT
BACKGROUND: In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. METHODS: We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. FINDINGS: Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9-19·2) and to hospital discharge to be 24·7 days (22·9-28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70â117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56-3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23-1·53), with substantially higher ratios in older age groups (0·32% [0·27-0·38] in those aged <60 years vs 6·4% [5·7-7·2] in those aged ≥60 years), up to 13·4% (11·2-15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4-3·5] in those aged <60 years [n=360] and 4·5% [1·8-11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39-1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0-37·6) in those aged 80 years or older. INTERPRETATION: These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. FUNDING: UK Medical Research Council.
Subject(s)
Coronavirus Infections/mortality , Pandemics/statistics & numerical data , Pneumonia, Viral/mortality , Adolescent , Adult , Aged , Aged, 80 and over , Betacoronavirus , COVID-19 , Child , Child, Preschool , China/epidemiology , Hospitalization/statistics & numerical data , Humans , Incidence , Infant , Infant, Newborn , Middle Aged , Models, Statistical , SARS-CoV-2 , Young AdultABSTRACT
Human infection with Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is driven by recurring dromedary-to-human spill-over events, leading decision-makers to consider dromedary vaccination. Dromedary vaccine candidates in the development pipeline are showing hopeful results, but gaps in our understanding of the epidemiology of MERS-CoV in dromedaries must be addressed to design and evaluate potential vaccination strategies. We aim to bring together existing measures of MERS-CoV infection in dromedary camels to assess the distribution of infection, highlighting knowledge gaps and implications for animal vaccination. We systematically reviewed the published literature on MEDLINE, EMBASE and Web of Science that reported seroprevalence and/or prevalence of active MERS-CoV infection in dromedary camels from both cross-sectional and longitudinal studies. 60 studies met our eligibility criteria. Qualitative syntheses determined that MERS-CoV seroprevalence increased with age up to 80-100% in adult dromedaries supporting geographically widespread endemicity of MERS-CoV in dromedaries in both the Arabian Peninsula and countries exporting dromedaries from Africa. The high prevalence of active infection measured in juveniles and at sites where dromedary populations mix should guide further investigation - particularly of dromedary movement - and inform vaccination strategy design and evaluation through mathematical modelling.