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Demongeot J, Magal P. Data-driven mathematical modeling approaches for COVID-19: A survey. Phys Life Rev 2024; 50:166-208. [PMID: 39142261 DOI: 10.1016/j.plrev.2024.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Accepted: 08/02/2024] [Indexed: 08/16/2024]
Abstract
In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).
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Affiliation(s)
- Jacques Demongeot
- Université Grenoble Alpes, AGEIS EA7407, La Tronche, F-38700, France.
| | - Pierre Magal
- Department of Mathematics, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China; Univ. Bordeaux, IMB, UMR 5251, Talence, F-33400, France; CNRS, IMB, UMR 5251, Talence, F-33400, France
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2
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Lee H, Choi H, Lee H, Lee S, Kim C. Uncovering COVID-19 transmission tree: identifying traced and untraced infections in an infection network. Front Public Health 2024; 12:1362823. [PMID: 38887240 PMCID: PMC11180726 DOI: 10.3389/fpubh.2024.1362823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 05/21/2024] [Indexed: 06/20/2024] Open
Abstract
Introduction This paper presents a comprehensive analysis of COVID-19 transmission dynamics using an infection network derived from epidemiological data in South Korea, covering the period from January 3, 2020, to July 11, 2021. The network illustrates infector-infectee relationships and provides invaluable insights for managing and mitigating the spread of the disease. However, significant missing data hinder conventional analysis of such networks from epidemiological surveillance. Methods To address this challenge, this article suggests a novel approach for categorizing individuals into four distinct groups, based on the classification of their infector or infectee status as either traced or untraced cases among all confirmed cases. The study analyzes the changes in the infection networks among untraced and traced cases across five distinct periods. Results The four types of cases emphasize the impact of various factors, such as the implementation of public health strategies and the emergence of novel COVID-19 variants, which contribute to the propagation of COVID-19 transmission. One of the key findings is the identification of notable transmission patterns in specific age groups, particularly in those aged 20-29, 40-69, and 0-9, based on the four type classifications. Furthermore, we develop a novel real-time indicator to assess the potential for infectious disease transmission more effectively. By analyzing the lengths of connected components, this indicator facilitates improved predictions and enables policymakers to proactively respond, thereby helping to mitigate the effects of the pandemic on global communities. Conclusion This study offers a novel approach to categorizing COVID-19 cases, provides insights into transmission patterns, and introduces a real-time indicator for better assessment and management of the disease transmission, thereby supporting more effective public health interventions.
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Affiliation(s)
- Hyunwoo Lee
- Department of Mathematics, Kyungpook National University, Daegu, Republic of Korea
- Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu, Republic of Korea
| | - Hayoung Choi
- Department of Mathematics, Kyungpook National University, Daegu, Republic of Korea
- Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu, Republic of Korea
| | - Hyojung Lee
- Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu, Republic of Korea
- Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
| | - Sunmi Lee
- Nonlinear Dynamics and Mathematical Application Center, Kyungpook National University, Daegu, Republic of Korea
- Department of Applied Mathematics, Kyunghee University, Yongin-si, Republic of Korea
| | - Changhoon Kim
- Department of Preventive Medicine, College of Medicine, Pusan National University, Busan, Republic of Korea
- Busan Center for Infectious Disease Control and Prevention, Pusan National University Hospital, Busan, Republic of Korea
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3
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Jiang N, Kolozsvary C, Li Y. Artificial Neural Network Prediction of COVID-19 Daily Infection Count. Bull Math Biol 2024; 86:49. [PMID: 38558267 DOI: 10.1007/s11538-024-01275-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 02/21/2024] [Indexed: 04/04/2024]
Abstract
This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output set of our training process. To achieve this, we implement a regularized backcasting technique that utilize death counts and the infection fatality ratio (IFR), as the death statistics and serological surveys (providing the IFR) as more reliable COVID-19 data sources. Addressing the impact of factors such as age distribution, vaccination, and emerging variants on the IFR time series is a pivotal aspect of our analysis. We expect our study to enhance our understanding of the genuine implications of the COVID-19 pandemic, subsequently benefiting mitigation strategies.
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Affiliation(s)
- Ning Jiang
- Department of Mathematics and Statistics, University of Massachusetts, 710 N Pleasant St, Amherst, 01003, MA, USA
| | - Charles Kolozsvary
- Department of Mathematics and Statistics, University of Massachusetts, 710 N Pleasant St, Amherst, 01003, MA, USA
| | - Yao Li
- Department of Mathematics and Statistics, University of Massachusetts, 710 N Pleasant St, Amherst, 01003, MA, USA.
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4
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Boldea O, Alipoor A, Pei S, Shaman J, Rozhnova G. Age-specific transmission dynamics of SARS-CoV-2 during the first 2 years of the pandemic. PNAS NEXUS 2024; 3:pgae024. [PMID: 38312225 PMCID: PMC10837015 DOI: 10.1093/pnasnexus/pgae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 01/09/2024] [Indexed: 02/06/2024]
Abstract
During its first 2 years, the SARS-CoV-2 pandemic manifested as multiple waves shaped by complex interactions between variants of concern, non-pharmaceutical interventions, and the immunological landscape of the population. Understanding how the age-specific epidemiology of SARS-CoV-2 has evolved throughout the pandemic is crucial for informing policy decisions. In this article, we aimed to develop an inference-based modeling approach to reconstruct the burden of true infections and hospital admissions in children, adolescents, and adults over the seven waves of four variants (wild-type, Alpha, Delta, and Omicron BA.1) during the first 2 years of the pandemic, using the Netherlands as the motivating example. We find that reported cases are a considerable underestimate and a generally poor predictor of true infection burden, especially because case reporting differs by age. The contribution of children and adolescents to total infection and hospitalization burden increased with successive variants and was largest during the Omicron BA.1 period. However, the ratio of hospitalizations to infections decreased with each subsequent variant in all age categories. Before the Delta period, almost all infections were primary infections occurring in naive individuals. During the Delta and Omicron BA.1 periods, primary infections were common in children but relatively rare in adults who experienced either reinfections or breakthrough infections. Our approach can be used to understand age-specific epidemiology through successive waves in other countries where random community surveys uncovering true SARS-CoV-2 dynamics are absent but basic surveillance and statistics data are available.
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Affiliation(s)
- Otilia Boldea
- Department of Econometrics and OR, Tilburg School of Economics and Management, Tilburg University, Tilburg 5037 AB, The Netherlands
| | - Amir Alipoor
- Department of Econometrics and OR, Tilburg School of Economics and Management, Tilburg University, Tilburg 5037 AB, The Netherlands
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
- Columbia Climate School, Columbia University, New York, NY 10025, USA
| | - Ganna Rozhnova
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht 3584 CX, The Netherlands
- Center for Complex Systems Studies (CCSS), Utrecht University, Utrecht 3584 CE, The Netherlands
- Faculdade de Ciências, Universidade de Lisboa, Lisbon PT1749-016, Portugal
- BioISI—Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon PT1749-016, Portugal
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5
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Stanke Z, Spouge JL. Estimating age-stratified transmission and reproduction numbers during the early exponential phase of an epidemic: A case study with COVID-19 data. Epidemics 2023; 44:100714. [PMID: 37595401 PMCID: PMC10528737 DOI: 10.1016/j.epidem.2023.100714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 06/07/2023] [Accepted: 08/08/2023] [Indexed: 08/20/2023] Open
Abstract
In a pending pandemic, early knowledge of age-specific disease parameters, e.g., susceptibility, infectivity, and the clinical fraction (the fraction of infections coming to clinical attention), supports targeted public health responses like school closures or sequestration of the elderly. The earlier the knowledge, the more useful it is, so the present article examines an early phase of many epidemics, exponential growth. Using age-stratified COVID-19 case counts collected in Canada, China, Israel, Italy, the Netherlands, and the United Kingdom before April 23, 2020, we present a linear analysis of the exponential phase that attempts to estimate the age-specific disease parameters given above. Some combinations of the parameters can be estimated by requiring that they change smoothly with age. The estimation yielded: (1) the case susceptibility, defined for each age-group as the product of susceptibility to infection and the clinical fraction; (2) the mean number of transmissions of infection per contact within each age-group; and (3) the reproduction number of infection within each age-group, i.e., the diagonal of the age-stratified next-generation matrix. Our restriction to data from the exponential phase indicates the combinations of epidemic parameters that are intrinsically easiest to estimate with early age-stratified case counts. For example, conclusions concerning the age-dependence of case susceptibility appeared more robust than corresponding conclusions about infectivity. Generally, the analysis produced some results consistent with conclusions confirmed much later in the COVID-19 pandemic. Notably, our analysis showed that in some countries, the reproduction number of infection within the half-decade 70-75 was unusually large compared to other half-decades. Our analysis therefore could have anticipated that without countermeasures, COVID-19 would spread rapidly once seeded in homes for the elderly.
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Affiliation(s)
- Zachary Stanke
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - John L Spouge
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
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6
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Gourieroux C, Jasiak J. Time varying Markov process with partially observed aggregate data: An application to coronavirus. JOURNAL OF ECONOMETRICS 2023; 232:35-51. [PMID: 33281272 PMCID: PMC7698670 DOI: 10.1016/j.jeconom.2020.09.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 06/08/2020] [Accepted: 09/15/2020] [Indexed: 05/20/2023]
Abstract
A major difficulty in the analysis of Covid-19 transmission is that many infected individuals are asymptomatic. For this reason, the total counts of infected individuals and of recovered immunized individuals are unknown, especially during the early phase of the epidemic. In this paper, we consider a parametric time varying Markov process of Coronavirus transmission and show how to estimate the model parameters and approximate the unobserved counts from daily data on infected and detected individuals and the total daily death counts. This model-based approach is illustrated in an application to French data, performed on April 6, 2020.
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Affiliation(s)
- C Gourieroux
- University of Toronto, Canada, Toulouse School of Economics and CREST, France
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7
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Fischer K, Reade JJ, Schmal WB. What cannot be cured must be endured: The long-lasting effect of a COVID-19 infection on workplace productivity. LABOUR ECONOMICS 2022; 79:102281. [PMID: 36217320 PMCID: PMC9535936 DOI: 10.1016/j.labeco.2022.102281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 09/29/2022] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
The COVID-19 pandemic has triggered economic shock waves across the globe. Exploiting a natural experiment, this paper estimates how being infected with the virus shapes individual-level productivity after having recovered. Studying the performance of professional athletes in Germany and Italy and applying a staggered difference-in-differences design, we find that individual performance drops by around 6 percent after a previously infected athlete returns to the pitch. This striking deterioration remains persistent over time - amounting to 5% eight months after the infection. The effect increases with age and infection severity, and is spread disproportionally over the course of a match. We detect no productivity effects for other respiratory infections. We take these findings as first evidence that the pandemic might cause long-lasting effects on worker productivity and economic growth.
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Affiliation(s)
- Kai Fischer
- Düsseldorf Institute for Competition Economics (DICE), Heinrich Heine University, Germany
| | - J James Reade
- Department of Economics, University of Reading, United Kingdom
| | - W Benedikt Schmal
- Düsseldorf Institute for Competition Economics (DICE), Heinrich Heine University, Germany
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8
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Zhunis A, Mai TD, Kim S. Responses to COVID-19 with probabilistic programming. Front Public Health 2022; 10:953472. [PMID: 36478717 PMCID: PMC9720399 DOI: 10.3389/fpubh.2022.953472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 11/01/2022] [Indexed: 11/22/2022] Open
Abstract
The COVID-19 pandemic left its unique mark on the twenty-first century as one of the most significant disasters in history, triggering governments all over the world to respond with a wide range of interventions. However, these restrictions come with a substantial price tag. It is crucial for governments to form anti-virus strategies that balance the trade-off between protecting public health and minimizing the economic cost. This work proposes a probabilistic programming method to quantify the efficiency of major initial non-pharmaceutical interventions. We present a generative simulation model that accounts for the economic and human capital cost of adopting such strategies, and provide an end-to-end pipeline to simulate the virus spread and the incurred loss of various policy combinations. By investigating the national response in 10 countries covering four continents, we found that social distancing coupled with contact tracing is the most successful policy, reducing the virus transmission rate by 96% along with a 98% reduction in economic and human capital loss. Together with experimental results, we open-sourced a framework to test the efficacy of each policy combination.
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Affiliation(s)
- Assem Zhunis
- School of Computing, KAIST, Daejeon, South Korea,Data Science Group, Institute for Basic Science, Daejeon, South Korea
| | - Tung-Duong Mai
- School of Computing, KAIST, Daejeon, South Korea,Data Science Group, Institute for Basic Science, Daejeon, South Korea,Samsung Electronics, Seoul, South Korea
| | - Sundong Kim
- Data Science Group, Institute for Basic Science, Daejeon, South Korea,AI Graduate School, GIST, Gwangju, South Korea,*Correspondence: Sundong Kim
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9
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Guccio C. Measuring Resilience and Fatality Rate During the First Wave of COVID-19 Pandemic in Northern Italy: A Note. Public Health Rev 2022; 43:1604308. [PMID: 35795654 PMCID: PMC9250972 DOI: 10.3389/phrs.2022.1604308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 05/24/2022] [Indexed: 11/15/2022] Open
Abstract
Background: This Policy Brief aims to contribute to the debate on the resilience of the healthcare systems during the pandemic by discussing whether mortality indicators are appropriate for assessing resilience or whether other statistics should be employed. Evidence: During the first wave of the COVID-19, much emphasis was placed on case-fatality rates to offer a preliminary assessment of the resilience of healthcare systems. However, these statistics are often biased and do not consider the real figure of the population that has been infected. Policy Options and Recommendations: Comparing data obtained with different approaches based on statistical inference and large-scale serological survey, the brief highlights, that great care must be taken when using case-fatality data, which in the absence of careful analysis, can lead to erroneous conclusions. Conclusion: Using case-fatality rate gives us no sounding information about the real capability of healthcare systems to save lives during the pandemic. However, even in the absence of detailed epidemiological data new advancements in statistical methods can be useful to provide a more sounding evaluation of the resilience of the healthcare systems.
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Affiliation(s)
- Calogero Guccio
- Department of Economics and Business, University of Catania, Catania, Italy
- Health Econometrics and Data Group, University of York, Heslington, United Kingdom
- *Correspondence: Calogero Guccio,
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10
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Feng Q, Wu GL, Yuan M, Zhou S. Save lives or save livelihoods? A cross-country analysis of COVID-19 pandemic and economic growth. JOURNAL OF ECONOMIC BEHAVIOR & ORGANIZATION 2022; 197:221-256. [PMID: 35287307 PMCID: PMC8907024 DOI: 10.1016/j.jebo.2022.02.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 02/19/2022] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
This paper studies whether containing COVID-19 pandemic by stringent strategies deteriorates or saves economic growth. Since there are country-specific factors that could affect both economic growth and deaths due to COVID-19, we first start with a cross-country analysis on identifying risk and protective factors on the COVID-19 deaths using large across-country variation. Using data on 100 countries from 3 January to 27 November 2020 and taking into account the possibility of underreporting, we find that for deaths per million population, GDP per capita, population density, and income inequality are the three most important risk factors; government effectiveness, temperature, and hospital beds are the three most important protective factors. Second, inspired by the stochastic frontier literature, we construct a measure of pandemic containment effectiveness (PCE) after controlling for country-specific factors and rank countries by their PCE scores for deaths. Finally, by linking the PCE score with GDP growth data in Quarters 2 and 3 of 2020, we find that PCE is positively associated with economic growth in major economies. Countries with average PCE scores, such as Malaysia, would gain more GDP growth by 3.47 percentage points if they could improve their PCE scores for deaths to South Korea's level in Q2 of 2020. Therefore, there is not a trade-off between lives and livelihood facing by governments. Instead, to save economy, it is important to contain the pandemic first. Our conclusion is also mainly valid for infections due to COVID-19.
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Affiliation(s)
- Qu Feng
- Economics Division, School of Social Sciences, Nanyang Technological University, 48 Nanyang Ave, 639818, Singapore
| | - Guiying Laura Wu
- Economics Division, School of Social Sciences, Nanyang Technological University, 48 Nanyang Ave, 639818, Singapore
| | - Mengying Yuan
- Economics Division, School of Social Sciences, Nanyang Technological University, 48 Nanyang Ave, 639818, Singapore
| | - Shihao Zhou
- Economics Division, School of Social Sciences, Nanyang Technological University, 48 Nanyang Ave, 639818, Singapore
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11
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Gearhart R, Sonchak-Ardan L, Michieka N. The efficiency of COVID cases to COVID policies: a robust conditional approach. EMPIRICAL ECONOMICS 2022; 63:2903-2948. [PMID: 35431413 PMCID: PMC8994571 DOI: 10.1007/s00181-022-02234-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 03/09/2022] [Indexed: 06/14/2023]
Abstract
This paper examines the efficiency of four major COVID-19 social distancing policies: (i) shelter-in-place orders (SIPO), (ii) non-essential business closures, (iii) mandatory quarantine for travelers, and (iv) bans on large gatherings. Results suggest that the average US state is highly inefficient in producing the fraction of the population that does not have COVID-19 without social distancing policies put in place. We find that having any of the four major social distancing policies increases conditional efficiency by 9.7 (9.5) percentage points in the first 100 days (full sample). This corresponds to 57 (172) fewer total COVID-19 cases per 100,000 population in the first 100 days (full sample). We also find that population density accounts for a majority of unconditional state inefficiency. Evidence suggests considerable heterogeneity in conditional efficiency improvement, indicating that no uniform national social distancing policy would have been more effective; more effective strategies would have been to target more densely populated areas. Conditional efficiency regressions suggest that bans on large gatherings were the most effective policies, with SIPOs and non-essential business closures having smaller impacts. States that implemented social distancing policies except mandatory quarantine for traveler policies were highly effective for the first 100 days, but had less effectiveness over the full sample. There is also preliminary evidence that premature revocations of social distancing policies reduced conditional efficiency, leading to COVID-19 case spikes.
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Affiliation(s)
- Richard Gearhart
- Associate Professor of Economics, School of Business and Public Administration, Department of Economics, California State University, 9001 Stockdale Highway, 20 BDC, Bakersfield, CA 93311 USA
| | - Lyudmyla Sonchak-Ardan
- Assistant Professor of Economics, Department of Economics, Susquehanna University, 514 University Ave., Selinsgrove, PA 17870 USA
| | - Nyakundi Michieka
- Associate Professor of Economics, School of Business and Public Administration, Department of Economics, California State University, 9001 Stockdale Highway, 20 BDC, Bakersfield, CA 93311 USA
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12
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Millimet DL, Parmeter CF. COVID-19 severity: A new approach to quantifying global cases and deaths. JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A, (STATISTICS IN SOCIETY) 2022; 185:RSSA12826. [PMID: 35600509 PMCID: PMC9115431 DOI: 10.1111/rssa.12826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 01/21/2022] [Indexed: 05/22/2023]
Abstract
As the COVID-19 pandemic has progressed, so too has the recognition that cases and deaths have been underreported, perhaps vastly so. Here, we present an econometric strategy to estimate the true number of COVID-19 cases and deaths for 61 and 56 countries, respectively, from 1 January 2020 to 3 November 2020. Specifically, we estimate a 'structural' model based on the SIR epidemiological model extended to incorporate underreporting. The results indicate significant underreporting by magnitudes that align with existing research and conjectures by public health experts. While our approach requires some strong assumptions, these assumptions are very different from the equally strong assumptions required by other approaches addressing underreporting in the assessment of the extent of the pandemic. Thus, we view our approach as a complement to existing methods.
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Chen J, Song JJ, Stamey JD. A Bayesian Hierarchical Spatial Model to Correct for Misreporting in Count Data: Application to State-Level COVID-19 Data in the United States. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:3327. [PMID: 35329019 PMCID: PMC8950980 DOI: 10.3390/ijerph19063327] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/27/2022] [Accepted: 03/05/2022] [Indexed: 02/01/2023]
Abstract
The COVID-19 pandemic that began at the end of 2019 has caused hundreds of millions of infections and millions of deaths worldwide. COVID-19 posed a threat to human health and profoundly impacted the global economy and people's lifestyles. The United States is one of the countries severely affected by the disease. Evidence shows that the spread of COVID-19 was significantly underestimated in the early stages, which prevented governments from adopting effective interventions promptly to curb the spread of the disease. This paper adopts a Bayesian hierarchical model to study the under-reporting of COVID-19 at the state level in the United States as of the end of April 2020. The model examines the effects of different covariates on the under-reporting and accurate incidence rates and considers spatial dependency. In addition to under-reporting (false negatives), we also explore the impact of over-reporting (false positives). Adjusting for misclassification requires adding additional parameters that are not directly identified by the observed data. Informative priors are required. We discuss prior elicitation and include R functions that convert expert information into the appropriate prior distribution.
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Affiliation(s)
| | | | - James D. Stamey
- Department of Statistical Science, Baylor University, Waco, TX 76798-7140, USA; (J.C.); (J.J.S.)
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14
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Chen X, Chong WF, Feng R, Zhang L. Pandemic risk management: Resources contingency planning and allocation. INSURANCE, MATHEMATICS & ECONOMICS 2021; 101:359-383. [PMID: 34803199 PMCID: PMC8593845 DOI: 10.1016/j.insmatheco.2021.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 06/13/2023]
Abstract
The repeated history of pandemics, such as SARS, H1N1, Ebola, Zika, and COVID-19, has shown that pandemic risk is inevitable. Extraordinary shortages of medical resources have been observed in many parts of the world. Some attributing factors include the lack of sufficient stockpiles and the lack of coordinated efforts to deploy existing resources to the locations of greatest need. This paper investigates contingency planning and resources allocation from a risk management perspective, as opposed to the prevailing supply chain perspective. The key idea is that the competition for limited critical resources is not only present in different geographical locations but also at different stages of a pandemic. This paper draws on an analogy between risk aggregation and capital allocation in finance and pandemic resources planning and allocation for healthcare systems. The main contribution is to introduce new strategies for optimal stockpiling and allocation balancing spatio-temporal competition for medical supply and demand.
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Affiliation(s)
| | - Wing Fung Chong
- Department of Mathematics and Department of Statistics, University of Illinois at Urbana-Champaign, United States of America
- Department of Actuarial Mathematics and Statistics, Heriot-Watt University, United Kingdom
| | - Runhuan Feng
- Department of Mathematics, University of Illinois at Urbana-Champaign, United States of America
| | - Linfeng Zhang
- Department of Mathematics, University of Illinois at Urbana-Champaign, United States of America
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15
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Olmo J, Sanso‐Navarro M. Modeling the spread of COVID-19 in New York City. PAPERS IN REGIONAL SCIENCE : THE JOURNAL OF THE REGIONAL SCIENCE ASSOCIATION INTERNATIONAL 2021; 100:1209-1229. [PMID: 34226811 PMCID: PMC8242800 DOI: 10.1111/pirs.12615] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/05/2021] [Accepted: 05/06/2021] [Indexed: 05/30/2023]
Abstract
This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVID-19 cases in the city of New York at zip code level. Within a Bayesian model averaging framework, the baseline is a Poisson regression for count data. The set of covariates includes autoregressive terms, spatial effects, and demographic and socioeconomic variables. Our results for the second wave of the coronavirus pandemic show that these regressors are more significant to predict the number of new confirmed cases as the pandemic unfolds. Both pointwise and interval forecasts exhibit strong predictive ability in-sample and out-of-sample.
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Affiliation(s)
- Jose Olmo
- Departamento de Análisis EconómicoUniversidad de ZaragozaSpain
- Department of EconomicsUniversity of SouthamptonUnited Kingdom
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Pang JK, Jones SP, Waite LL, Olson NA, Armstrong JW, Atmur RJ, Cummins JJ. Probability and estimated risk of SARS-CoV-2 transmission in the air travel system. Travel Med Infect Dis 2021; 43:102133. [PMID: 34182036 PMCID: PMC8233548 DOI: 10.1016/j.tmaid.2021.102133] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 06/17/2021] [Accepted: 06/22/2021] [Indexed: 11/18/2022]
Abstract
BACKGROUND As an emerging virus, SARS-CoV-2 and the risk of transmission during air travel is of high interest. This paper is a retrospective estimate of the probability of an infectious passenger in the air travel system transmitting the SARS-CoV-2 virus to a fellow passenger. METHODS Literature was reviewed from May-September 2020 to identify COVID-19 cases related to air travel. The studies were limited to publicly available literature for passengers; studies of flight crews were not reviewed. A novel quantitative approach was developed to estimate air travel transmission risk that considers secondary cases, the overall passenger population, and correction factors for asymptomatic transmission and underreporting. RESULTS There were at least 2866 index infectious passengers documented to have passed through the air travel system in a 1.4 billion passenger population. Using correction factors, the global risk of transmission during air travel is estimated at 1:1.7 million; acknowledging that assumptions exist around case detection rate and mass screenings. Uncertainty in the correction factors and a 95% credible interval indicate risk ranges from 1 case for every 712,000 travelers to 1 case for every 8 million travelers. CONCLUSION The risk of COVID-19 transmission on an aircraft is low, even with infectious persons onboard.
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Girgis SA, Hafez HM, Elarab HE, Sherif B, Sabry MH, Afifi I, Hassan FE, Reda A, Elsayed S, Mahmoud A, Habeb P, Habil IS, Hussein RS, Mossad IM, Mansour O, Omar A, Saleh AM, El-Meteini M. SARS-CoV-2 PCR positivity rate and seroprevalence of related antibodies among a sample of patients in Cairo: Pre-wave 2 results of a screening program in a university hospital. PLoS One 2021; 16:e0254581. [PMID: 34265021 PMCID: PMC8282003 DOI: 10.1371/journal.pone.0254581] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 06/29/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Research has revealed that asymptomatic and pre-symptomatic infections are important contributors to the transmission of SARS-CoV-2 in populations. In Egypt, the true prevalence of infections is veiled due to the low number of screening tests. The aim of this study was to determine the SARS-CoV-2 PCR positivity rate as well the seroprevalence of the SARS-CoV-2 antibodies before the ultimate development of a second wave of the epidemic in Cairo, Egypt. METHODS Our study was carried out between May 5 and the end of October 2020. It included all patients requiring admission to Ain Shams University hospitals. An interview questionnaire was used to collect demographic and clinical data. Laboratory tests for all participants included RT-PCR and total antibody assay for SARS-CoV-2. RESULTS A total of 4,313 subjects were enrolled in our study, with females representing 56% of the sample. Adults and middle-aged individuals represented around 60% of the study sample. The positivity rate of SARS-CoV-2 PCR was 3.84% (95% CI 3.29-4.48), and the SARS-CoV-2 antibody seroprevalence was 29.82% (95% CI: 28.16-31.51). Males showed a higher risk for getting the COVID-19 infection, while middle-age group had significantly higher antibody seroprevalence rates. CONCLUSION SARS-CoV-2 infection imposes a high burden on the community as detected by high seroprevalence rates.
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Affiliation(s)
- Samia A. Girgis
- Professor of Clinical Pathology, Faculty of Medicine Director of Infection Control Unit and Vice Director of Ain Shams University Hospitals, Cairo, Egypt
| | - Hala M. Hafez
- Professor of Clinical Pathology, Faculty of Medicine, Head of Clinical Microbiology Unit, Ain Shams University Hospitals, Cairo, Egypt
| | - Hoda Ezz Elarab
- Department of Clinical Pathology, Ain Shams University Hospitals, Cairo, Egypt
| | - Basma Sherif
- Department of Clinical Pathology, Ain Shams University Hospitals, Cairo, Egypt
| | - Moshira H. Sabry
- Infection Control Unit, Ain Shams University Hospitals, Cairo, Egypt
| | - Iman Afifi
- Infection Control Unit, Ain Shams University Hospitals, Cairo, Egypt
| | | | - Amira Reda
- Infection Control Unit, Ain Shams University Hospitals, Cairo, Egypt
| | - Shaimaa Elsayed
- Infection Control Unit, Ain Shams University Hospitals, Cairo, Egypt
| | - Asmaa Mahmoud
- Infection Control Unit, Ain Shams University Hospitals, Cairo, Egypt
| | - Petra Habeb
- Infection Control Unit, Ain Shams University Hospitals, Cairo, Egypt
| | - Ihab S. Habil
- Department of Community, Environmental and Occupational Medicine, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Rasha S. Hussein
- Department of Community, Environmental and Occupational Medicine, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Isis M. Mossad
- Department of Community, Environmental and Occupational Medicine, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Ossama Mansour
- Vice Dean of Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Ashraf Omar
- Dean of Faculty of Medicine and chairman of board of Ain Shams University Hospitals, Ain Shams University, Cairo, Egypt
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Yu X, Zhang Y, Sun H. Modeling COVID-19 spreading dynamics and unemployment rate evolution in rural and urban counties of Alabama and New York using fractional derivative models. RESULTS IN PHYSICS 2021; 26:104360. [PMID: 34642619 PMCID: PMC8495489 DOI: 10.1016/j.rinp.2021.104360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 05/13/2023]
Abstract
The COVID-19 pandemic has been affecting the United States (U.S.) since the outbreak documented on 2/29/2020, and understanding its dynamics is critical for pandemic mitigation and economic recovery. This study proposed and applied novel time fractional derivative models (FDMs) to quantify the spatiotemporal dynamics of the COVID-19 pandemic spreading in the states of Alabama and New York, U.S., two states with quite different population compositions, urbanization, and industry structures. Model applications revealed that the pandemic evolving in the two states exhibited an overall similar time-dependent trend with subtle differences in propagation rates. Alabama may have more inter-county communications in rural areas than urban areas, while the opposite may be true for the New York State. Further analysis using the space FDM showed that the COVID-19 pandemic spread in rural/urban areas of the two states by following the tempered stable density distributions with different indexes, while the number of the state's pandemic epicenters affected the pattern of the COVID-19 pandemic spreading in space. Finally, applications of a novel time FDM revealed that the evolution of the economy, represented by the weekly unemployment insurance claims in the two states, exhibited different spreading and recovery rates, most likely due to their different exposures and responses to the pandemic. Therefore, COVID-19 spreading dynamics exhibited strong and subtly different spatiotemporal memories in rural and urban areas in the Alabama and New York States, motivating the application of FDMs.
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Affiliation(s)
- Xiangnan Yu
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, Jiangsu 210098, China
- College of Mechanics and Materials, Hohai University, Nanjing 210098, China
| | - Yong Zhang
- Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA
| | - HongGuang Sun
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, Jiangsu 210098, China
- College of Mechanics and Materials, Hohai University, Nanjing 210098, China
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Sallahi N, Park H, El Mellouhi F, Rachdi M, Ouassou I, Belhaouari S, Arredouani A, Bensmail H. Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar. BIOLOGY 2021; 10:biology10060463. [PMID: 34073810 PMCID: PMC8225146 DOI: 10.3390/biology10060463] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/16/2021] [Accepted: 05/19/2021] [Indexed: 12/24/2022]
Abstract
Simple Summary A modified SIR model was applied to provide COVID-19 pandemic analysis and predictions for Gulf Cooperation Council countries, as well as representative countries in Europe and New York City. We estimated reported, infected, and unreported cases from cumulative reported cases and simulated data. We also estimated the basic reproduction rates at different phases of the pandemic. Outputs show that the modified SIR model fits very well with the outcome of the COVID-19 pandemic for the studied countries and could be generalized to other countries. The model prediction emphasizes the value of significant interventions in public health in regulating the epidemic taking into account that a constant fraction of the infected cases remain unreported during the pandemic. We report and analyze the effectiveness of preventive/intervention measures applied to the overall community to curb the severity of the pandemic. Our model could be used to support public health authorities with respect to post-outbreak reopening decisions, highlighting effective measures that need to be maintained, eased, or implemented to support safe reopening strategies in the GCC countries. Abstract Epidemiological Modeling supports the evaluation of various disease management activities. The value of epidemiological models lies in their ability to study various scenarios and to provide governments with a priori knowledge of the consequence of disease incursions and the impact of preventive strategies. A prevalent method of modeling the spread of pandemics is to categorize individuals in the population as belonging to one of several distinct compartments, which represents their health status with regard to the pandemic. In this work, a modified SIR epidemic model is proposed and analyzed with respect to the identification of its parameters and initial values based on stated or recorded case data from public health sources to estimate the unreported cases and the effectiveness of public health policies such as social distancing in slowing the spread of the epidemic. The analysis aims to highlight the importance of unreported cases for correcting the underestimated basic reproduction number. In many epidemic outbreaks, the number of reported infections is likely much lower than the actual number of infections which can be calculated from the model’s parameters derived from reported case data. The analysis is applied to the COVID-19 pandemic for several countries in the Gulf region and Europe.
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Affiliation(s)
- Narjiss Sallahi
- National Institute of Posts and Telecommunications (INPT), Rabat 210024, Morocco;
| | - Heesoo Park
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar; (H.P.); (F.E.M.)
| | - Fedwa El Mellouhi
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar; (H.P.); (F.E.M.)
| | - Mustapha Rachdi
- Data Sciences Project, University of Grenoble, 38400 Grenoble, France;
| | - Idir Ouassou
- University Qaddi Ayyad, Marrakech 40000, Morocco;
| | - Samir Belhaouari
- ICT Department, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar;
| | - Abdelilah Arredouani
- Diabetes Department, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar;
| | - Halima Bensmail
- Data Analytics Department, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar
- Correspondence: ; Tel.: +974-5527-8824
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20
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Abstract
Regional quarantine policies, in which a portion of a population surrounding infections is locked down, are an important tool to contain disease. However, jurisdictional governments-such as cities, counties, states, and countries-act with minimal coordination across borders. We show that a regional quarantine policy's effectiveness depends on whether 1) the network of interactions satisfies a growth balance condition, 2) infections have a short delay in detection, and 3) the government has control over and knowledge of the necessary parts of the network (no leakage of behaviors). As these conditions generally fail to be satisfied, especially when interactions cross borders, we show that substantial improvements are possible if governments are outward looking and proactive: triggering quarantines in reaction to neighbors' infection rates, in some cases even before infections are detected internally. We also show that even a few lax governments-those that wait for nontrivial internal infection rates before quarantining-impose substantial costs on the whole system. Our results illustrate the importance of understanding contagion across policy borders and offer a starting point in designing proactive policies for decentralized jurisdictions.
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21
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Noland RB. Mobility and the effective reproduction rate of COVID-19. JOURNAL OF TRANSPORT & HEALTH 2021; 20:101016. [PMID: 33542894 PMCID: PMC7843082 DOI: 10.1016/j.jth.2021.101016] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 01/08/2021] [Accepted: 01/20/2021] [Indexed: 05/04/2023]
Abstract
OBJECTIVES Due to the infectiousness of COVID-19, the mobility of individuals has sharply decreased, both in response to government policy and self-protection. This analysis seeks to understand how mobility reductions reduce the spread of the coronavirus (SAR-CoV-2), using readily available data sources. METHODS Mobility data from Google is correlated with estimates of the effective reproduction rate, R t , which is a measure of viral infectiousness (Google, 2020). The Google mobility data provides estimates of reductions in mobility, for six types of trips and activities. R t for US states are downloaded from an on-line platform that derives daily estimates based on data from the Covid Tracking Project (Wissel et al., 2020; Systrom et al., 2020). Fixed effects models are estimated relating mean R t and 80% upper level credible interval estimates to changes in mobility and a time-trend value and with both 7-day and 14-day lags. RESULTS All mobility variables are correlated with median R t and the upper level credible interval of R t . Staying at home is effective at reducing R t, . Time spent at parks has a small positive effect, while other activities all have larger positive effects. The time trend is negative suggesting increases in self-protective behavior. Predictions suggest that returning to baseline levels of activity for retail, transit, and workplaces, will increase R t above 1.0, but not for other activities. Mobility reductions of about 20-40% are needed to achieve an R t below 1.0 (for the upper level 80% credible interval) and even larger reductions to achieve an R t below 0.7. CONCLUSIONS Policy makers need to be cautious with encouraging return to normal mobility behavior, especially returns to workplaces, transit, and retail locations. Activity at parks appears to not increase R t as much. This research also demonstrates the value of using on-line data sources to conduct rapid policy-relevant analysis of emerging issues.
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Affiliation(s)
- Robert B Noland
- Alan M. Voorhees Transportation Center, Edward J. Bloustein School of Planning and Public Policy, Rutgers University, New Brunswick, NJ, 08901, United States
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22
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Peng Z, Ao S, Liu L, Bao S, Hu T, Wu H, Wang R. Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:1090. [PMID: 33530563 PMCID: PMC7908085 DOI: 10.3390/ijerph18031090] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 01/18/2021] [Accepted: 01/22/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. METHODS Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. RESULTS Based on the US county-level COVID-19 data from 22 January (T1) to 20 August (T212) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007-0.157 (mean = 0.048), 7.31-185.6 (mean = 38.89), and 0.04-2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7-14.0) and IFR was 0.70% (95%CI 0.52-0.95%) at T212. INTERPRETATION Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.
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Affiliation(s)
- Zhenghong Peng
- Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China; (Z.P.); (S.A.); (H.W.); (R.W.)
| | - Siya Ao
- Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China; (Z.P.); (S.A.); (H.W.); (R.W.)
| | - Lingbo Liu
- Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China
| | - Shuming Bao
- China Data Institute, Ann Arbor, MI 48108, USA;
| | - Tao Hu
- Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA;
| | - Hao Wu
- Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China; (Z.P.); (S.A.); (H.W.); (R.W.)
| | - Ru Wang
- Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China; (Z.P.); (S.A.); (H.W.); (R.W.)
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Unnikrishnan J, Mangalathu S, Kutty RV. Estimating under-reporting of COVID-19 cases in Indian states: an approach using a delay-adjusted case fatality ratio. BMJ Open 2021; 11:e042584. [PMID: 33472784 PMCID: PMC7818846 DOI: 10.1136/bmjopen-2020-042584] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 12/20/2020] [Accepted: 12/30/2020] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVES The COVID-19 pandemic has spread to all states in India. Due to limitations in testing coverage, the true extent of the spread may not be fully reflected in the reported cases. In this study, we obtain time-varying estimates of the fraction of COVID-19 infections reported in the different states. METHODS Following a methodology developed in prior work, we use a delay-adjusted case fatality ratio to estimate the true fraction of cases reported in different states. We also develop a delay adjusted test positivity estimation method and study the relationship between the estimated test positivity rate for each state and the estimated fraction of cases reported. SETTING We apply this method of analysis to all Indian states reporting at least 100 deaths as of 10 October 2020. RESULTS Our analysis suggests that delay-adjusted case fatality ratios observed in different states range from 0.47% to 3.55%. The estimated fraction of cases reported in different states ranges from 39% to 100% for an assumed baseline case fatality ratio of 1.38%, from 18.6% to 100% for an assumed baseline case fatality ratio of 0.66%, and from 2.8% to 19.7% for an assumed baseline case fatality ratio of 0.1%. We also demonstrate a statistically significant negative relationship between the fraction of cases reported in each state and the testing positivity rate. CONCLUSIONS The estimates provide a means to quantify and compare the trends of reporting and the true level of current infections in different states. This information may be used to guide policies for prioritising testing in different states, and also to analyse the time-varying effects of different quarantine measures adopted in different states.
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Affiliation(s)
| | | | - Raman V Kutty
- Amala Cancer Research Center, Amalanagar, Thrissur, Kerala, India
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Böttcher L, D’Orsogna MR, Chou T. Using excess deaths and testing statistics to improve estimates of COVID-19 mortalities. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.01.10.21249524. [PMID: 33469606 PMCID: PMC7814852 DOI: 10.1101/2021.01.10.21249524] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Factors such as non-uniform definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US is 13% higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find negligible or negative excess deaths for part and all of 2020 for Denmark, Germany, and Norway.
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Affiliation(s)
- Lucas Böttcher
- Dept. of Computational Medicine, UCLA, Los Angeles, CA 90095-1766
| | - Maria R. D’Orsogna
- Dept. of Computational Medicine, UCLA, Los Angeles, CA 90095-1766
- Dept. of Mathematics, California State University at Northridge, Los Angeles, CA 91330-8313
| | - Tom Chou
- Dept. of Computational Medicine, UCLA, Los Angeles, CA 90095-1766
- Dept. of Mathematics, UCLA, Los Angeles, CA 90095-1555
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Böttcher L, D’Orsogna MR, Chou T. Using excess deaths and testing statistics to improve estimates of COVID-19 mortalities. ARXIV 2021:arXiv:2101.03467v1. [PMID: 33442558 PMCID: PMC7805454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Factors such as non-uniform definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US is 13$\%$ higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find negligible or negative excess deaths for part and all of 2020 for Denmark, Germany, and Norway.
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Affiliation(s)
- Lucas Böttcher
- Dept. of Computational Medicine, UCLA, Los Angeles, CA 90095-1766
| | - Maria R. D’Orsogna
- Dept. of Computational Medicine, UCLA, Los Angeles, CA 90095-1766
- Dept. of Mathematics, California State University at Northridge, Los Angeles, CA 91330-8313
| | - Tom Chou
- Dept. of Computational Medicine, UCLA, Los Angeles, CA 90095-1766
- Dept. of Mathematics, UCLA, Los Angeles, CA 90095-1555
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Toulis P. Estimation of Covid-19 prevalence from serology tests: A partial identification approach. JOURNAL OF ECONOMETRICS 2021; 220:193-213. [PMID: 33100477 PMCID: PMC7574757 DOI: 10.1016/j.jeconom.2020.10.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We propose a partial identification method for estimating disease prevalence from serology studies. Our data are results from antibody tests in some population sample, where the test parameters, such as the true/false positive rates, are unknown. Our method scans the entire parameter space, and rejects parameter values using the joint data density as the test statistic. The proposed method is conservative for marginal inference, in general, but its key advantage over more standard approaches is that it is valid in finite samples even when the underlying model is not point identified. Moreover, our method requires only independence of serology test results, and does not rely on asymptotic arguments, normality assumptions, or other approximations. We use recent Covid-19 serology studies in the US, and show that the parameter confidence set is generally wide, and cannot support definite conclusions. Specifically, recent serology studies from California suggest a prevalence anywhere in the range 0%-2% (at the time of study), and are therefore inconclusive. However, this range could be narrowed down to 0.7%-1.5% if the actual false positive rate of the antibody test was indeed near its empirical estimate ( ∼ 0.5%). In another study from New York state, Covid-19 prevalence is confidently estimated in the range 13%-17% in mid-April of 2020, which also suggests significant geographic variation in Covid-19 exposure across the US. Combining all datasets yields a 5%-8% prevalence range. Our results overall suggest that serology testing on a massive scale can give crucial information for future policy design, even when such tests are imperfect and their parameters unknown.
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Affiliation(s)
- Panos Toulis
- University of Chicago, Booth School of Business, United States of America
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Bird JJ, Barnes CM, Premebida C, Ekárt A, Faria DR. Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach. PLoS One 2020; 15:e0241332. [PMID: 33112931 PMCID: PMC7592809 DOI: 10.1371/journal.pone.0241332] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 10/13/2020] [Indexed: 12/23/2022] Open
Abstract
In this work we present a three-stage Machine Learning strategy to country-level risk classification based on countries that are reporting COVID-19 information. A K% binning discretisation (K = 25) is used to create four risk groups of countries based on the risk of transmission (coronavirus cases per million population), risk of mortality (coronavirus deaths per million population), and risk of inability to test (coronavirus tests per million population). The four risk groups produced by K% binning are labelled as 'low', 'medium-low', 'medium-high', and 'high'. Coronavirus-related data are then removed and the attributes for prediction of the three types of risk are given as the geopolitical and demographic data describing each country. Thus, the calculation of class label is based on coronavirus data but the input attributes are country-level information regardless of coronavirus data. The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. It is noted that high risk for inability to test is often coupled with low risks for transmission and mortality, therefore the risk of inability to test should be interpreted first, before consideration is given to the predicted transmission and mortality risks. Finally, the approach is applied to more recent risk levels to data from September 2020 and weaker results are noted due to the growth of international collaboration detracting useful knowledge from country-level attributes which suggests that similar machine learning approaches are more useful prior to situations later unfolding.
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Affiliation(s)
- Jordan J. Bird
- Aston Robotics, Vision, and Intelligent Systems Lab (ARVIS), School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom
| | - Chloe M. Barnes
- Aston Robotics, Vision, and Intelligent Systems Lab (ARVIS), School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom
| | - Cristiano Premebida
- Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal
| | - Anikó Ekárt
- Aston Robotics, Vision, and Intelligent Systems Lab (ARVIS), School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom
| | - Diego R. Faria
- Aston Robotics, Vision, and Intelligent Systems Lab (ARVIS), School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom
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Kim J, Ashihara K. National Disaster Management System: COVID-19 Case in Korea. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17186691. [PMID: 32937946 PMCID: PMC7559530 DOI: 10.3390/ijerph17186691] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/06/2020] [Accepted: 09/08/2020] [Indexed: 11/16/2022]
Abstract
The COVID-19 pandemic poses unprecedented challenges for governments and societies around the world and represents a global crisis of hitherto unexperienced proportions. Our research seeks to analyse disaster management systems from a national perspective by examining the Korean management of the COVID-19 crisis according to a four-phase epidemiological disaster management system. Utilising a meta-study, official documents, reports and interviews, we explore the role of the control tower mechanism related to the life-cycle of disaster management, and Korea’s sustainable containment strategy. This study begins with a discussion of the crisis and disaster management literature and provides specific information related to the Korean government’s response to COVID-19. It continues by detailing specific strategies such as wide-spread testing, tracking, treatment and quarantine that have enabled Korea to prevent wide-spread community transmission. The study concludes emphasising the relevance of systematic national disaster management, providing insight into methods for containment in Korea – a system commended by the WHO. Implications include the extension and the efficient application of disaster management theory by empirical application and integration of concepts.
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Affiliation(s)
- Junic Kim
- Correspondence: ; Tel.: +82-2-450-3484
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Smetters KA. Stay-at-home orders and second waves: a graphical exposition. THE GENEVA RISK AND INSURANCE REVIEW 2020; 45:94-103. [PMID: 32929319 PMCID: PMC7481546 DOI: 10.1057/s10713-020-00056-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 08/27/2020] [Indexed: 06/11/2023]
Abstract
Integrated epidemiological-economics models have recently appeared to study optimal government policy, especially stay-at-home orders (mass "quarantines"). But these models are challenging to interpret due to the lack of closed-form solutions. This note provides an intuitive and graphical explanation of optimal quarantine policy. To be optimal, a quarantine requires "the cavalry" (e.g., mass testing, strong therapeutics, or a vaccine) to arrive just in time, not too early or too late. The graphical explanation accommodates numerous extensions, including hospital constraints, sick worker, age differentiation, and learning. The effect of uncertainty about the arrival time of "the cavalry" is also discussed.
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