True Number Of Infections Research Articles

Estimating actual SARS-CoV-2 infections from secondary data

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation—rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

Forecasting of COVID-19 spread in Poland as part of health needs mapping

Abstract The development of a robust model for COVID-19 forecasting was a crucial task undertaken by the Ministry of Health of the Republic of Poland in response to the pandemic. High-quality forecasting rendered the inclusion of COVID-19 modelling in health needs assessment indispensable at the regional as well as country level. The model served as a basis for data-driven policy-making, such as the introduction or lifting of non-pharmaceutical interventions and the management of hospital resource allocation. The proposed model draws upon the population-adjusted infection fatality rates, vaccine-induced immunity, reported test positivity rates, and COVID-19-related mortality to infer the dark figure of cases. The estimates of the true number of infections lay the foundations for the computation of variant-dependent effective reproduction numbers, which constitute the workhorse of the model. The algorithm utilises the Bayesian prediction of those metrics along with the estimation of hybrid immunity levels to arrive at a short-term forecast of the course of the pandemic. The implemented model yields reliable results throughout the outbreak, allowing for the assessment of the strain of consecutive epidemic waves on the healthcare system. Key messages • Forecasting of COVID-19 spread is vital for health needs assessment and health crisis management policy-making. • Short-term forecasting of COVID-19 spread using effective reproduction numbers yields high-quality results.

Estimating the cumulative incidence of SARS-CoV-2 infection in Costa Rica: modelling seroprevalence data in a population-based cohort

The true incidence of SARS-CoV-2 infection in Costa Rica was likely much higher than officially reported, because infection is often associated with mild symptoms and testing was limited by official guidelines and socio-economic factors. Using serology to define natural infection, we developed a statistical model to estimate the true cumulative incidence of SARS-CoV-2 in Costa Rica early in the pandemic. We estimated seroprevalence from 2223 blood samples collected from November 2020 to October 2021 from 1976 population-based controls from the RESPIRA study. Samples were tested for antibodies against SARS-CoV-2 nucleocapsid and the receptor-binding-domain of the spike proteins. Using a generalized linear model, we estimated the ratio of true infections to officially reported cases. Applying these ratios to officially reported totals by age, sex, and geographic area, we estimated the true number of infections in the study area, where 70% of Costa Ricans reside. We adjusted the seroprevalence estimates for antibody decay over time, estimated from 1562 blood samples from 996 PCR-confirmed COVID-19 cases. The estimated total proportion infected (ETPI) was 4.0 times higher than the officially reported total proportion infected (OTPI). By December 16th, 2021, the ETPI was 47% [42-52] while the OTPI was 12%. In children and adolescents, the ETPI was 11.0 times higher than the OTPI. Our findings suggest that nearly half the population had been infected by the end of 2021. By the end of 2022, it is likely that a large majority of the population had been infected. This work was sponsored and funded by the National Institute of Allergy and Infectious Diseases through the National Cancer Institute, the Science, Innovation, Technology and Telecommunications Ministry of Costa Rica, and Costa Rican Biomedical Research Agency-Fundacion INCIENSA (grant N/A).

Seroprevalence of SARS-CoV-2(Covid-19) Antibody Among Blood Donors in a Tertiary Care Centre in South India

Introduction The novel severe acute respiratory syndrome virus 2 (SARS-CoV-2), which is responsible Coronavirus disease (COVID-19), spread worldwide from China, causing a pandemic from late December 2019. Due to the high proportion of asymptomatic or mild infections (approximately 80%), data restricted to laboratory-confirmed cases do not capture the true extent of the spread or burden of the virus, or its infection-fatality ratio. Therefore, serological detection of specific antibodies against SARS-CoV-2 can better estimate the true number of infections. The current study aims to estimate the seroprevalence of SARS-CoV-2 antibodies among the whole blood donors without any prior COVID-19 history or symptoms. Objective To determine seroprevalence of SARS-CoV-2 (COVID-19) antibody (IgG and IgM) among asymptomatic healthy blood donors. Methods This was a cross sectional study conducted between March and July, 2021 among 300 blood donors without any prior COVID-19 history or symptoms who came to a tertiary care, multispecialty hospital in south India. Any donor who had recently travelled abroad or donors who had received COVID-19 vaccine are excluded from the study. 3 ml venous blood was drawn in EDTA tube from participants and was tested by “Access SARS CoV-2 IgG assay” and “Access SARS CoV-2 IgM assay” by UniCel DxI 800 Immunoassay analyzer (Beckman coulter). The Access SARS CoV-2 IgG assay and the Access SARS Cov-2 IgM assay detect antibodies to the Receptor Binding Domain (RBD) of the Spike Protein. Result was reported as Reactive if Signal/Cut-off (S/CO)>1.0 and non-Reactive if S/CO <1. Data was collected and entered into excel sheets and was analyzed by using the software SPSS version 25. Results A total of 300 healthy blood donors were included. The study reported seroprevalence of 15.3% for IgG and 4.3% for IgM (95%CI) among asymptomatic whole blood donors. No significant difference was observed across age groups, diet, BMI, ABO/Rh blood type or Ayurveda/homeo immune medicine intake with respect to IgG and IgM reactivity. Conclusion 15% of blood donors were seroconverted for COVID-19 during second wave. This reflects widespread seroprevalence in the adult population. Real-time seroprevalence studies will help to know the herd immunity among the blood donors which will assist in knowing the COVID-19 transmission dynamics and distribution of immunity levels at a particular point in time.

SARS-CoV-2 Seroprevalence in Preschool and School-Age Children.

The SARS-CoV-2 pandemic is ongoing in Germany. Children and adolescents are increasingly being infected, and many cases presumably remain undetected and unreported. Sero-epidemiological studies can help estimate the true number of infections. From January 2020 to June 2022, 59 786 persons aged 1-17 years were tested for SARS-CoV-2 antibodies as part of a screening program for presymptomatic type 1 diabetes in the German federal state of Bavaria (the Fr1da study). In June 2022, the seroprevalence in the overall population was 73.5%. The seroprevalence was significantly higher in school-age children (from 5 to 10 years of age) than in preschool children (ages 1-4): 84.4% vs. 66.6%, p <0.001. In contrast, in November 2021, before the appearance of the omicron variant, the overall seroprevalence was 14.7% (16.2% of school-age children, 13.0% of preschool children, p = 0.06). In the overall collective, seroprevalence increased fivefold from the fall of 2021 to June 2022 (by a factor of 5.2 in school-age children and 5.1 in preschool children). Similar seroprevalences, with smaller case numbers, were observed in June 2022 in the corresponding Fr1da studies in Saxony and Northern Germany: 87.8% and 76.7%, respectively. Monthly case counts reveal a substantial rise in SARS-CoV-2-infections among children and adolescents from late 2021 to mid-2022. The high percentage of preschool and school-age children who have been infected with SARS-CoV-2, in a population that has low vaccination coverage, should be taken into account in the development of health policies.

Estimating the undetected infections in the Monkeypox outbreak.

While the number of detected Monkeypox infections are widely available, an understanding of the extent of undetected cases is urgently needed for an effective tackling of its spread. The aim of this study is to estimate the true number of Monkeypox (detected and undetected) infections in most affected countries. The question being asked is: How many cases have actually occurred? We propose a lower bound estimator for the true number of Monkeypox cases. The estimator is data-driven and can be easily computed from the cumulative distributions of weekly cases. We focused on the ratio of the total estimated cases to the observed cases on July 31, 2022: The proportion of undetected cases was relevant in all countries, with countries whose estimated true number of infections could be more than three times the observed one. We provided a practical contribution to the understanding of the current Monkeypox wave and reliable estimates on how many undetected cases are going around in several countries, where the epidemic spreads differently.

Anti-SARS-CoV-2 seroprevalence in King County, WA—Cross-sectional survey, August 2020

We conducted a seroprevalence survey to estimate the true number of infections with SARS-CoV-2, the virus that causes COVID-19, in King County as of August 2020 by measuring the proportion of residents from who had antibodies against the virus. Participants from 727 households took part in a cross-sectional address-based household survey with random and non-random samples and provided dried blood spots that were tested for total antibody against the viral nucleocapsid protein, with confirmatory testing for immunoglobulin G against the spike protein. The data were weighted to match King County’s population based on sex, age group, income, race, and Hispanic status. After weighting and accounting for the accuracy of the tests, our best overall estimate of anti-SARS-CoV-2 seroprevalence in King County as of August 2020 is 3.9% (95% confidence interval (CI) 2.4%-6.0%) with an effective sample size of 589. Comparing seroprevalence with positive test reports, our survey suggests that viral testing underestimated incidence by a factor of about five and suggests that the proportion of cases that were serious (based on hospitalization) or fatal was 2.4% and 0.8%, respectively. Prevalence varied by subgroup; households reporting incomes at or below $100,000 in 2019 had nearly five times higher estimated antibody prevalence than those with incomes above $100,000. Those reporting non-White/non-Asian race had roughly seven times higher estimated antibody prevalence than those reporting White race. This survey was noteworthy for including people of all ages; among all age groups, the weighted estimate of prevalence was highest in older teens and young adults and lowest in young children, although these differences were not statistically significant.

Inferring the true number of SARS-CoV-2 infections in Japan

IntroductionIn Japan, as of December 31, 2021, more than 1.73 million laboratory-confirmed cases have been reported. However, the actual number of infections is likely to be under-ascertained due to the epidemiological characteristics such as mild and subclinical infections and limited testing availability in the early days of the pandemic. In this study, we infer the true number of infections in Japan between January 16, 2020, and December 31, 2021, using a statistical modelling framework that combines data on reported cases and fatalities. MethodsWe used reported COVID-19 deaths and age-specific infection fatality ratios (IFR) to impute the true number of infections. Estimates of IFR were informed from published studies and were adjusted to reflect the effects of pharmaceutical interventions, mass vaccination, and evolving variants. To account for the uncertainty in IFR, we sampled values from relevant distributions. ResultsWe estimated that as of December 31, 2021, 3.07 million (CrI: 2.05–4.24 million) people had been infected in Japan, which is 1.77 times higher than the 1.73 million reported cases. Our meta-analysis confirmed that these findings were consistent with the intermittent seroprevalence studies conducted in Japan. ConclusionsWe have estimated that a substantial number of COVID-19 infections in Japan were unreported, particularly in adults. Our approach provides a more realistic assessment of the true underlying burden of COVID-19. The results of this study can be used as fundamental components to strengthen population health control and surveillance measures.

Estimating unconfirmed COVID-19 infection cases and multiple waves of pandemic progression with consideration of testing capacity and non-pharmaceutical interventions: A dynamic spreading model

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has unique epidemiological characteristics that include presymptomatic and asymptomatic infections, resulting in a large proportion of infected cases being unconfirmed, including patients with clinical symptoms who have not been identified by screening. These unconfirmed infected individuals move and spread the virus freely, presenting difficult challenges to the control of the pandemic. To reveal the actual pandemic situation in a given region, a simple dynamic susceptible-unconfirmed-confirmed-removed (D-SUCR) model is developed taking into account the influence of unconfirmed cases, the testing capacity, the multiple waves of the pandemic, and the use of non-pharmaceutical interventions. Using this model, the total numbers of infected cases in 51 regions of the USA and 116 countries worldwide are estimated, and the results indicate that only about 40% of the true number of infections have been confirmed. In addition, it is found that if local authorities could enhance their testing capacities and implement a timely strict quarantine strategy after identifying the first infection case, the total number of infected cases could be reduced by more than 90%. Delay in implementing quarantine measures would drastically reduce their effectiveness.

Seroprevalence of antibodies against SARS-CoV-2 in the adult population during the pre-vaccination period, Norway, winter 2020/21.

BackgroundSince March 2020, 440 million people worldwide have been diagnosed with COVID-19, but the true number of infections with SARS-CoV-2 is higher. SARS-CoV-2 antibody seroprevalence can add crucial epidemiological information about population infection dynamics.AimTo provide a large population-based SARS-CoV-2 seroprevalence survey from Norway; we estimated SARS-CoV-2 seroprevalence before introduction of vaccines and described its distribution across demographic groups.MethodsIn this population-based cross-sectional study, a total of 110,000 people aged 16 years or older were randomly selected during November–December 2020 and invited to complete a questionnaire and provide a dried blood spot (DBS) sample.ResultsThe response rate was 30% (31,458/104,637); compliance rate for return of DBS samples was 88% (27,700/31,458). National weighted and adjusted seroprevalence was 0.9% (95% CI (confidence interval): 0.7–1.0). Seroprevalence was highest among those aged 16–19 years (1.9%; 95% CI: 0.9–2.9), those born outside the Nordic countries 1.4% (95% CI: 1.0–1.9), and in the counties of Oslo 1.7% (95% CI: 1.2–2.2) and Vestland 1.4% (95% CI: 0.9–1.8). The ratio of SARS-CoV-2 seroprevalence (0.9%) to cumulative incidence of virologically detected cases by mid-December 2020 (0.8%) was slightly above one. SARS-CoV-2 seroprevalence was low before introduction of vaccines in Norway and was comparable to virologically detected cases, indicating that most cases in the first 10 months of the pandemic were detected.ConclusionFindings suggest that preventive measures including contact tracing have been effective, people complied with physical distancing recommendations, and local efforts to contain outbreaks have been essential.

Seroprevalence of SARS-CoV-2 IgG antibodies in a healthcare setting during the first pandemic wave in Senegal

This study investigated the seroprevalence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) immunoglobulin G (IgG) during the first pandemic wave in Senegal. The seroprevalence rate of SARS-CoV-2 IgG was assessed in 10 cities in Senegal by testing plasma from volunteers attending healthcare clinics for reasons unrelated to coronavirus disease 2019 (n=3231) between June and October 2020. The overall positivity rate was 20.4% and large geographical differences in seropositivity (6–41.9%) were observed, suggesting that the true number of infections was substantially higher than the official estimate of 8.5%.

SARS-CoV-2 Seroprevalence in Germany.

Until now, information on the spread of SARS-CoV-2 infections in Germany has been based mainly on data from the public health offices. It may be assumed that these data do not include many cases of asymptomatic and mild infection. We determined seroprevalence over the course of the pandemic in a sequential, multilocal seroprevalence study (MuSPAD). Study participants were recruited at random in seven administrative districts (Kreise) in Germany from July 2020 onward; each participant was tested at two different times 3-5 months apart. Test findings on blood samples were used to determine the missed-case rate of reported infections, the infection fatality rate (IFR), and the association between seropositivity and demographic, socio-economic, and health-related factors, as well as to evaluate the self-reported results of PCR and antigenic tests. The registration number of this study is DRKS00022335. Among non-vaccinated persons, the seroprevalence from July to December 2020 was 1.3-2.8% and rose between February and May 2021 to 4.1-13.1%. In July 2021, 35% of tested persons in Chemnitz were not vaccinated, and the seroprevalence among these persons was 32.4% (07/2021). The surveillance detection ratio (SDR), i.e., the ratio between the true number of infections estimated from seroprevalence and the actual number or reported infections, varied among the districts included in the study from 2.2 to 5.1 up to December 2020 and from 1.3 to 2.9 up to June 2021, and subsequently declined. The IFR was in the range of 0.8% to 2.4% in all regions except Magdeburg, where a value of 0.3% was calculated for November 2020. A lower educational level was associated with a higher seropositivity rate, smoking with a lower seropositivity rate. On average, 1 person was infected for every 8.5 persons in quarantine. Seroprevalence was low after the first wave of the pandemic but rose markedly during the second and third waves. The missed-case rate trended downward over the course of the pandemic.

What does COVID-19 testing results really say? The real statistics concealed behind the accessible data.

Accurate and comprehensive testing is crucial for practitioners to portray the pandemic. Without testing there is no data; yet, the exact number of infected people cannot be determined due to the lack of comprehensive testing. The number of seropositive for SARS‐CoV‐2 infection is obviously relative to the extent of testing. However, the true number of infections might be still far higher than the reported values. To compare the countries based on the number of seropositive for SARS‐CoV‐2 infection is misleading, as there may not be enough tests being carried out to properly monitor the outbreak. In this paper, we closely look through the COVID‐19 testing results. Herein, we try to draw conclusions based on the reported data: first, the presence of a possible relationship between COVID‐19 transition and patients' age will be assessed. Then, the COVID‐19 case fatality rate (CFR) is compared with the age‐demographic data for different countries. Based on the results, a method for estimating a lower bound (minimum) for the number of actual positive cases will be developed and validated. Results of this study have shown that CFR is a metric reflecting the spread of the virus, but is a factor of the extent of testing and does not necessarily show the real size of the outbreak. Moreover, no large difference in susceptibility by age has been found. The results suggest the similarity between the age distribution of COVID‐19 and the population age‐demographic is improving over the course of the pandemic. In addition, countries with lower CFRs have a more similar COVID‐19 age distribution, which is a result of more comprehensive testing. Finally, a method for estimation of the real number of infected people based on the age distributions, reported CFRs, and the extent of testing will be developed and validated.

Dynamic estimation of epidemiological parameters of COVID-19 outbreak and effects of interventions on its spread.

Background: A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter.Methods: We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794–22,372] and 124,506 [95% CI 69,526–265,113], respectively.Results: The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34–3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65–0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan.Conclusions: This case study by our proposed method affirms the believed importance and effectiveness of imposing tight nonessential travel restrictions and affirm the importance and effectiveness of government interventions (e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread.Significance for public healthIn fighting global pandemic such as COVID-19, an important early task for understanding the spread is to closely monitor the infection size and assess the disease epidemiological parameters. The in- sights gained from the epidemiological parameter estimation enable public health practitioners to dynamically monitor the temporal spread trend and to quantitatively analyze the effectiveness of new public health policies. In this paper, we aim to address a key technical challenge potentially arising from the under-reporting issues in pandemic early periods, and critically re-examine the COVID-19 situation at the initial epicenter Wuhan city as a practically relevant case study. Methodological development for modeling dynamic evolution involving parameter estimation therefore has important public health applications and is expected to have significant impact on modeling practice for understanding future epidemic events well beyond COVID-19.

Enhanced surveillance of COVID-19 in Scotland: population-based seroprevalence surveillance for SARS-CoV-2 during the first wave of the epidemic

ObjectivesThe impact of the COVID-19 pandemic in Scotland has been amongst the most severe in Europe. Serological surveillance is critical to determine the overall extent of infection across populations and to inform the public health response. This study aimed to estimate the proportion of people who have antibodies to SARS-CoV-2 (‘seroprevalence’) in the general population of Scotland and to see if this changes over time. Study design/MethodsBetween International Organization for Standardization (ISO) week 17 (i.e. week commencing 20th April) and ISO week 25 (week commencing 15 June), 4751 residual blood samples were obtained from regional biochemistry laboratories in six participating regional health authority areas covering approximately 75% of the Scottish population. Samples were tested for the presence of anti-SARS-CoV-2 IgG antibodies using the LIAISON®SARS-CoV-2 S1/S2 IgG assay (DiaSorin, Italy). Seroprevalence rates were adjusted for the sensitivity and specificity of the assay using Bayesian methods. ResultsThe combined adjusted seroprevalence across the study period was 4.3% (95% confidence interval: 4.2%–4.5%). The proportion varied each week between 1.9% and 6.8% with no difference in antibody positivity by age, sex or geographical area. ConclusionsAt the end of the first wave of the COVID-19 pandemic, only a small fraction of the Scottish population had antibodies to SARS-CoV-2. Control of COVID-19 requires the ability to detect asymptomatic and mild infections that would otherwise remain undetected through existing surveillance systems. This is important to determine the true number of infections within the general population which, in turn, can help to understand transmission, inform control measures and provide a denominator for the estimation of severity measures such as the proportion of infected people who have been hospitalised and/or have died.

Estimating the Number of SARS-CoV-2 Infections and the Impact of Mitigation Policies in the United States

Knowledge of the number of individuals who have been infected with the novel coronavirus SARS-CoV-2 and the extent to which attempts for mitigation by executive order have been effective at limiting its spread are critical for effective policy going forward. Directly assessing prevalence and policy effects is complicated by the fact that case counts are unreliable. In this article, we present a model for using death-only data—in our opinion, the most stable and reliable source of COVID-19 information—to estimate the underlying epidemic curves. Our model links observed deaths to a susceptible-infected-removed (SIR) model of disease spread via a likelihood that accounts for the lag in time from infection to death and the infection fatality rate. We present estimates of the extent to which confirmed cases in the United States undercount the true number of infections, and analyze how effective social distancing orders have been at mitigating or suppressing the virus. We provide analysis for four states with significant epidemics: California, Florida, New York, and Washington.

SARS-CoV-2 seroprevalence in COVID-19 hotspots

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has surprised the world with its range of disease manifestations, from asymptomatic infection to critical illness leading to hospital admission and death.1,2 Due to the high proportion of asymptomatic or mild infections (approximately 80%), data restricted to laboratory-confirmed cases do not capture the true extent of the spread or burden of the virus, or its infection-fatality ratio.2 Therefore, serological detection of specific antibodies against SARS-CoV-2 can better estimate the true number of infections.

Host distribution and pathogen infection of fleas (Siphonaptera) recovered from small mammals in Pennsylvania.

The number of recognized flea-borne pathogens has increased over the past decade. However, the true number of infections related to all flea-borne pathogens remains unknown. To better understand the enzootic cycle of flea-borne pathogens, fleas were sampled from small mammals trapped in central Pennsylvania. A total of 541 small mammals were trapped, with white-footed mice (Peromyscus leucopus) and southern red-backed voles (Myodes gapperi) accounting for over 94% of the captures. Only P. leucopus were positive for examined blood-borne pathogens, with 47 (18.1%) and ten (4.8%) positive for Anaplasma phagocytophilum and Babesia microti, respectively. In addition, 61 fleas were collected from small mammals and tested for pathogens. Orchopeas leucopus was the most common flea and Bartonella vinsonii subspecies arupensis, B. microti, and a Rickettsia felis-like bacterium were detected in various flea samples. To the best of our knowledge, this is the first report of B. microti DNA detected from a flea and the first report of a R. felis-like bacterium from rodent fleas in eastern North America. This study provides evidence of emerging pathogens found in fleas, but further investigation is required to resolve the ecology of flea-borne disease transmission cycles.

Dynamic Estimation of Epidemiological Parameters of COVID-19 Outbreak and Effects of Interventions on Its Spread

A key challenge for estimating the epidemiological parameters of the COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from Wuhan to infer the true number in the city. This approach can only provide a static estimate of the epidemiological parameters before Wuhan lockdown on January 23, 2020, because there are almost no exported cases thereafter. Here, we propose a method to dynamically estimate the epidemiological parameters of the COVID-19 outbreak in Wuhan by recovering true numbers of infections from day-to-day official numbers. Using the method, we provide a comprehensive retrospection on how the disease had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794--22,372] and 124,506 [95% CI 69,526--265,113], respectively. The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34--3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65--0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan. In particular, transportation suspension and large scale hospitalization respectively prevented 33,719 and 90,072 people from getting infected in the nine-day time period right after its implementation.

Estimating Infectious Disease Underreporting at the Country Level: A Model and Application to the COVID-19 Pandemic

Background: Underreporting of infectious diseases is a pervasive challenge in public health that has emerged as a central issue in characterizing the dynamics of the COVID-19 pandemic. Estimating reporting accuracy via analytical methods is not always feasible due to time and resource constraints. We developed a model that predicts reporting completeness at the country/pathogen level from input variables accessible from the literature. Methods: We performed a literature search that collected estimates of reporting completeness for 32 pathogens, representing reporting estimates from 52 countries. We combined epidemiological and social science theory to identify factors that may influence empirical reporting rates, developed a model that can be used to estimate reporting completeness. We then applied this model to estimate reporting rates for SARS-CoV-2. Findings: Pathogen- and country-specific factors were predictive of reporting rates. Country epidemic preparedness was positively associated with reporting completeness, while countries with high levels of media bias in favor of incumbent governments were less likely to report infectious disease cases. Deadlier pathogens and sexually transmitted pathogens were more likely to be reported. Our model predicts that COVID-19 cases are widely underreported, with the true number of infections ranging from approximately 2·5 to 43 times the reported cases.Interpretation: Underreporting is a complex phenomenon that is driven by pathogen-level, health system, and political factors. This model can characterize and correct for the uncertainty in reported infectious disease statistics, particularly when outbreak-specific empirical estimates of underreporting are unavailable. More precise estimates can inform control policies and improve the accuracy of infectious disease models.Funding: None reported.Declaration of Interests: No competing interests to declare.