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Catano-Lopez A, Rojas-Diaz D, Lizarralde-Bejarano DP, Puerta Yepes ME. A discrete model for the evaluation of public policies: The case of Colombia during the COVID-19 pandemic. PLoS One 2023; 18:e0275546. [PMID: 36787303 PMCID: PMC9928135 DOI: 10.1371/journal.pone.0275546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/19/2022] [Indexed: 02/15/2023] Open
Abstract
In mathematical epidemiology, it is usual to implement compartmental models to study the transmission of diseases, allowing comprehension of the outbreak dynamics. Thus, it is necessary to identify the natural history of the disease and to establish promissory relations between the structure of a mathematical model, as well as its parameters, with control-related strategies (real interventions) and relevant socio-cultural behaviors. However, we identified gaps between the model creation and its implementation for the use of decision-makers for policy design. We aim to cover these gaps by proposing a discrete mathematical model with parameters having intuitive meaning to be implemented to help decision-makers in control policy design. The model considers novel contagion probabilities, quarantine, and diffusion processes to represent the recovery and mortality dynamics. We applied mathematical model for COVID-19 to Colombia and some of its localities; moreover, the model structure could be adapted for other diseases. Subsequently, we implemented it on a web platform (MathCOVID) for the usage of decision-makers to simulate the effect of policies such as lock-downs, social distancing, identification in the contagion network, and connectivity among populations. Furthermore, it was possible to assess the effects of migration and vaccination strategies as time-dependent inputs. Finally, the platform was capable of simulating the effects of applying one or more policies simultaneously.
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Affiliation(s)
| | - Daniel Rojas-Diaz
- Department of Mathematical Sciences, Universidad EAFIT, Medellín, Colombia
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2
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Benavides EM, Ordobás Gavín M, Mallaina García R, de Miguel García S, Ortíz Pinto M, Doménech Gimenez R, Gandarillas Grande A. COVID-19 dynamics in Madrid (Spain): A new convolutional model to find out the missing information during the first three waves. PLoS One 2022; 17:e0279080. [PMID: 36548226 PMCID: PMC9778560 DOI: 10.1371/journal.pone.0279080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
This article presents a novel mathematical model to describe the spread of an infectious disease in the presence of social and health events: it uses 15 compartments, 7 convolution integrals and 4 types of infected individuals, asymptomatic, mild, moderate and severe. A unique feature of this work is that the convolutions and the compartments have been selected to maximize the number of independent input parameters, leading to a 56-parameter model where only one had to evolve over time. The results show that 1) the proposed mathematical model is flexible and robust enough to describe the complex dynamic of the pandemic during the first three waves of the COVID-19 spread in the region of Madrid (Spain) and 2) the proposed model allows us to calculate the number of asymptomatic individuals and the number of persons who presented antibodies during the first waves. The study shows that the following results are compatible with the reported data: close to 28% of the infected individuals were asymptomatic during the three waves, close to 29% of asymptomatic individuals were detected during the subsequent waves and close to 26% of the Madrid population had antibodies at the end of the third wave. This calculated number of persons with antibodies is in great agreement with four direct measurements obtained from an independent sero-epidemiological research. In addition, six calculated curves (total number of confirmed cases, asymptomatic who are confirmed as positive, hospital admissions and discharges and intensive care units admissions) show good agreement with data from an epidemiological surveillance database.
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Affiliation(s)
- Efrén M. Benavides
- Department of Fluid Mechanics and Aersospace Propulsion, Universidad Politécnica de Madrid, Madrid, Spain
- * E-mail:
| | - María Ordobás Gavín
- Epidemiology Department, Directorate General of Public Health, Madrid Regional Health Authority, Madrid, Spain
| | - Raúl Mallaina García
- Strategic Planning Department, Directorate of Integrated Healthcare Process, Foundation on Innovation and Research in Primary Care Foundation FIIBAP, Madrid, Spain
| | - Sara de Miguel García
- Epidemiology Department, Directorate General of Public Health, Madrid Regional Health Authority, Madrid, Spain
| | - Maira Ortíz Pinto
- Epidemiology Department, Directorate General of Public Health, Madrid Regional Health Authority, Madrid, Spain
| | - Ramón Doménech Gimenez
- Epidemiology Department, Directorate General of Public Health, Madrid Regional Health Authority, Madrid, Spain
| | - Ana Gandarillas Grande
- Epidemiology Department, Directorate General of Public Health, Madrid Regional Health Authority, Madrid, Spain
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3
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da Costa Avelar PH, Del Coco N, Lamb LC, Tsoka S, Cardoso-Silva J. A Bayesian predictive analytics model for improving long range epidemic forecasting during an infection wave. HEALTHCARE ANALYTICS (NEW YORK, N.Y.) 2022; 2:100115. [PMID: 37520620 PMCID: PMC9533637 DOI: 10.1016/j.health.2022.100115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/17/2022] [Accepted: 09/26/2022] [Indexed: 11/04/2022]
Abstract
Following the outbreak of the coronavirus epidemic in early 2020, municipalities, regional governments and policymakers worldwide had to plan their Non-Pharmaceutical Interventions (NPIs) amidst a scenario of great uncertainty. At this early stage of an epidemic, where no vaccine or medical treatment is in sight, algorithmic prediction can become a powerful tool to inform local policymaking. However, when we replicated one prominent epidemiological model to inform health authorities in a region in the south of Brazil, we found that this model relied too heavily on manually predetermined covariates and was too reactive to changes in data trends. Our four proposed models access data of both daily reported deaths and infections as well as take into account missing data (e.g., the under-reporting of cases) more explicitly, with two of the proposed versions also attempting to model the delay in test reporting. We simulated weekly forecasting of deaths from the period from 31/05/2020 until 31/01/2021, with first week data being used as a cold-start to the algorithm, after which we use a lighter variant of the model for faster forecasting. Because our models are significantly more proactive in identifying trend changes, this has improved forecasting, especially in long-range predictions and after the peak of an infection wave, as they were quicker to adapt to scenarios after these peaks in reported deaths. Assuming reported cases were under-reported greatly benefited the model in its stability, and modelling retroactively-added data (due to the "hot" nature of the data used) had a negligible impact on performance.
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Affiliation(s)
- Pedro Henrique da Costa Avelar
- Data Science Brigade, Porto Alegre, Rio Grande do Sul, Brazil
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
- Department of Informatics, King's College London, London, United Kingdom
- Machine Intellection Department, Institute for Infocomm Research, A*STAR, Singapore
| | | | - Luis C Lamb
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Sophia Tsoka
- Department of Informatics, King's College London, London, United Kingdom
| | - Jonathan Cardoso-Silva
- Data Science Brigade, Porto Alegre, Rio Grande do Sul, Brazil
- Department of Informatics, King's College London, London, United Kingdom
- Data Science Institute, London School of Economics and Political Science, London, United Kingdom
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4
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COVID-19 vaccination policies under uncertain transmission characteristics using stochastic programming. PLoS One 2022; 17:e0270524. [PMID: 35867667 PMCID: PMC9307213 DOI: 10.1371/journal.pone.0270524] [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: 01/14/2022] [Accepted: 06/10/2022] [Indexed: 12/01/2022] Open
Abstract
We develop a new stochastic programming methodology for determining optimal vaccination policies for a multi-community heterogeneous population. An optimal policy provides the minimum number of vaccinations required to drive post-vaccination reproduction number to below one at a desired reliability level. To generate a vaccination policy, the new method considers the uncertainty in COVID-19 related parameters such as efficacy of vaccines, age-related variation in susceptibility and infectivity to SARS-CoV-2, distribution of household composition in a community, and variation in human interactions. We report on a computational study of the new methodology on a set of neighboring U.S. counties to generate vaccination policies based on vaccine availability. The results show that to control outbreaks at least a certain percentage of the population should be vaccinated in each community based on pre-determined reliability levels. The study also reveals the vaccine sharing capability of the proposed approach among counties under limited vaccine availability. This work contributes a decision-making tool to aid public health agencies worldwide in the allocation of limited vaccines under uncertainty towards controlling epidemics through vaccinations.
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5
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Truszkowska A, Zino L, Butail S, Caroppo E, Jiang Z, Rizzo A, Porfiri M. Predicting the Effects of Waning Vaccine Immunity Against COVID-19 through High-Resolution Agent-Based Modeling. ADVANCED THEORY AND SIMULATIONS 2022; 5:2100521. [PMID: 35540703 PMCID: PMC9073999 DOI: 10.1002/adts.202100521] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/24/2022] [Indexed: 02/06/2023]
Abstract
The potential waning of the vaccination immunity to COVID-19 could pose threats to public health, as it is tenable that the timing of such waning would synchronize with the near-complete restoration of normalcy. Should also testing be relaxed, a resurgent COVID-19 wave in winter 2021/2022 might be witnessed. In response to this risk, an additional vaccine dose, the booster shot, is being administered worldwide. A projected study with an outlook of 6 months explores the interplay between the rate at which boosters are distributed and the extent to which testing practices are implemented, using a highly granular agent-based model tuned on a medium-sized US town. Theoretical projections indicate that the administration of boosters at the rate at which the vaccine is currently administered could yield a severe resurgence of the pandemic. Projections suggest that the peak levels of mid-spring 2021 in the vaccination rate may prevent such a scenario to occur, although exact agreement between observations and projections should not be expected due to the continuously evolving nature of the pandemic. This study highlights the importance of testing, especially to detect asymptomatic individuals in the near future, as the release of the booster reaches full speed.
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Affiliation(s)
- Agnieszka Truszkowska
- Center for Urban Science and Progress, Tandon School of EngineeringNew York University370 Jay StreetBrooklynNY11201USA
- Department of Mechanical and Aerospace Engineering, Tandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
| | - Lorenzo Zino
- Faculty of Science and EngineeringUniversity of GroningenNijenborgh 4Groningen9747AGThe Netherlands
| | - Sachit Butail
- Department of Mechanical EngineeringNorthern Illinois UniversityDeKalbIL60115USA
| | - Emanuele Caroppo
- Department of Mental HealthLocal Health Unit ROMA 2Rome00159Italy
- University Research Center He.R.A.Universitá Cattolica del Sacro CuoreRome00168Italy
| | - Zhong‐Ping Jiang
- Department of Electrical and Computer Engineering, Tandon School of EngineeringNew York University370 Jay StreetBrooklynNY11201USA
| | - Alessandro Rizzo
- Department of Electronics and TelecommunicationsPolitecnico di TorinoTurin10129Italy
- Institute for Invention, Innovation and Entrepreneurship, Tandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
| | - Maurizio Porfiri
- Center for Urban Science and Progress, Tandon School of EngineeringNew York University370 Jay StreetBrooklynNY11201USA
- Department of Mechanical and Aerospace Engineering, Tandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
- Department of Biomedical Engineering, Tandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA
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6
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Operation Status Comparison Monitoring of China’s Southeast Asian Industrial Parks before and after COVID-19 Using Nighttime Lights Data. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11020122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
COVID-19 has had a huge impact on many industries around the world. Internationally-funded enterprises have been greatly affected by COVID-19 prevention and control measures, such as border controls. However, few studies have examined the impact of COVID-19 on internationally-funded enterprises. To this end, this paper considered 12 of China’s industrial parks situated in Southeast Asia, while comparing the operation status before and after the outbreak of COVID-19 based on remote sensing of nighttime lights (NTL). The NTL is generally used as a proxy for economic activity. First, six parameters were proposed to quantify and monitor the operation status based on NTL data. Subsequently, these parameters were calculated for the parks and for 10 km buffer zones surrounding them to analyze the differences in operating conditions. The results showed that (1) despite the negative impact of COVID-19, 9 out of the 12 parks had a mean NTL greater than 1, indicating that these parks are in better operating condition in 2020 than 2019; (2) 7 out of the 10 km buffer zones around the parks showed a decline in mean NTL. Only three parks showed a decline in mean NTL. The impact of COVID-19 on surrounding areas was greater than the impact on parks.
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7
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Martcheva M, Tuncer N, Ngonghala CN. Effects of social-distancing on infectious disease dynamics: an evolutionary game theory and economic perspective. JOURNAL OF BIOLOGICAL DYNAMICS 2021; 15:342-366. [PMID: 34182892 DOI: 10.1080/17513758.2021.1946177] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 06/12/2021] [Indexed: 05/20/2023]
Abstract
We propose two models inspired by the COVID-19 pandemic: a coupled disease-human behaviour (or disease-game theoretic), and a coupled disease-human behaviour-economic model, both of which account for the impact of social-distancing on disease control and economic growth. The models exhibit rich dynamical behaviour including multistable equilibria, a backward bifurcation, and sustained bounded periodic oscillations. Analyses of the first model suggests that the disease can be eliminated if everybody practices full social-distancing, but the most likely outcome is some level of disease coupled with some level of social-distancing. The same outcome is observed with the second model when the economy is weaker than the social norms to follow health directives. However, if the economy is stronger, it can support some level of social-distancing that can lead to disease elimination.
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Affiliation(s)
- Maia Martcheva
- Department of Mathematics, University of Florida, Gainesville, FL, USA
| | - Necibe Tuncer
- Department of Mathematics, Florida Atlantic University, Boca Raton, FL, USA
| | - Calistus N Ngonghala
- Department of Mathematics, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
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Lu Z, Wahlström J, Nehorai A. Containing epidemics in a local cluster via antidote distribution and partial quarantine. Phys Rev E 2021; 104:034307. [PMID: 34654168 DOI: 10.1103/physreve.104.034307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/26/2021] [Indexed: 11/07/2022]
Abstract
The study of spreading phenomena in networks, in particular the spread of disease, has attracted considerable interest in the network science research community. In this paper, we show that the outbreak of an epidemic can be effectively contained and suppressed in a small subnetwork by a combination of antidote distribution and partial quarantine. We improve over existing antidote distribution schemes based on personalized PageRank in two ways. First, we replace the constraint on the topology of this subnetwork described by Chung et al. [Internet Math. 6, 237 (2009)1542-795110.1080/15427951.2009.10129184] that a large fraction of the value of the personalized PageRank vector must be contained in the local cluster, with a partial quarantine scheme. Second, we derive a different lower bound on the amount of antidote. We show that, under our antidote distribution scheme, the probability of the infection spreading to the whole network is bounded, and the infection inside the subnetwork will disappear after a period that is proportional to the logarithm of the number of initially infected nodes. We demonstrate the effectiveness of our strategy with numerical simulations of epidemics on benchmark networks. We also test our strategy on two examples of epidemics in real-world networks. Our strategy is dependent only on the rate of infection, the rate of recovery, and the topology around the initially infected nodes, and is independent of the rest of the network.
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Affiliation(s)
- Zhenqi Lu
- Preston M. Green Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, USA
| | - Johan Wahlström
- Department of Computer Science, University of Exeter, Exeter EX4 4QF, United Kingdom
| | - Arye Nehorai
- Preston M. Green Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, USA
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Truszkowska A, Thakore M, Zino L, Butail S, Caroppo E, Jiang Z, Rizzo A, Porfiri M. Designing the Safe Reopening of US Towns Through High-Resolution Agent-Based Modeling. ADVANCED THEORY AND SIMULATIONS 2021; 4:2100157. [PMID: 34514293 PMCID: PMC8420460 DOI: 10.1002/adts.202100157] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/19/2021] [Indexed: 12/13/2022]
Abstract
As COVID‐19 vaccine is being rolled out in the US, public health authorities are gradually reopening the economy. To date, there is no consensus on a common approach among local authorities. Here, a high‐resolution agent‐based model is proposed to examine the interplay between the increased immunity afforded by the vaccine roll‐out and the transmission risks associated with reopening efforts. The model faithfully reproduces the demographics, spatial layout, and mobility patterns of the town of New Rochelle, NY — representative of the urban fabric of the US. Model predictions warrant caution in the reopening under the current rate at which people are being vaccinated, whereby increasing access to social gatherings in leisure locations and households at a 1% daily rate can lead to a 28% increase in the fatality rate within the next three months. The vaccine roll‐out plays a crucial role on the safety of reopening: doubling the current vaccination rate is predicted to be sufficient for safe, rapid reopening.
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Affiliation(s)
- Agnieszka Truszkowska
- Center for Urban Science and Progress Tandon School of Engineering New York University 370 Jay Street Brooklyn NY 11201 USA
- Department of Mechanical and Aerospace Engineering Tandon School of Engineering New York University Six MetroTech Center Brooklyn NY 11201 USA
| | - Malav Thakore
- Department of Mechanical Engineering Northern Illinois University DeKalb IL 60115 USA
| | - Lorenzo Zino
- Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen 9747 AG The Netherlands
| | - Sachit Butail
- Department of Mechanical Engineering Northern Illinois University DeKalb IL 60115 USA
| | - Emanuele Caroppo
- Department of Mental Health Local Health Unit ROMA 2 Rome 00159 Italy
- University Research Center He.R.A. Università Cattolica del Sacro Cuore Rome 00168 Italy
| | - Zhong‐Ping Jiang
- Department of Electrical and Computer Engineering Tandon School of Engineering New York University 370 Jay Street Brooklyn NY 11201 USA
| | - Alessandro Rizzo
- Department of Electronics and Telecommunications Politecnico di Torino Turin 10129 Italy
- Office of Innovation Tandon School of Engineering New York University Six MetroTech Center Brooklyn NY 11201 USA
| | - Maurizio Porfiri
- Center for Urban Science and Progress Tandon School of Engineering New York University 370 Jay Street Brooklyn NY 11201 USA
- Department of Mechanical and Aerospace Engineering Tandon School of Engineering New York University Six MetroTech Center Brooklyn NY 11201 USA
- Department of Biomedical Engineering Tandon School of Engineering New York University Six MetroTech Center Brooklyn NY 11201 USA
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Seroprevalence of SARS-CoV-2 Infection among Occupational Groups from the Bucaramanga Metropolitan Area, Colombia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18084172. [PMID: 33920843 PMCID: PMC8071134 DOI: 10.3390/ijerph18084172] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/07/2021] [Accepted: 04/08/2021] [Indexed: 12/17/2022]
Abstract
The negative effects of coronavirus disease 2019 (COVID-19) pandemic have impacted the world economy due to the absence from work because of SARS-CoV-2 infection in workers, among other reasons. However, some economic areas are essential to society and people must continue working outside the home to support economic reactivation; their serological profile could be different from that of the global population. Cross-sectional study: Workers from health, construction, public transportation, public force, bike delivery messengers, independent or informal commerce areas, and residents of Bucaramanga or its metropolitan area were invited to participate. All participants self-completed a virtual survey and a blood test was taken to assess IgG and IgM with the ARC COV2 test. Seroprevalence was estimated considering a complex survey design, correcting for a finite population effect and adjusting for test performance. A total of 7045 workers were enrolled; 59.9% were women and most were residents of Bucaramanga and working in health occupations. The global adjusted seroprevalence was 19.5% (CI: 95% 18.6–20.4), being higher for Girón (27.9%; 95% CI: 24.5–31.30). Workers with multiple contact with people during working hours or using public transportation to go to work had a higher frequency of seropositivity for SARS-CoV-2. The seroprevalence among workers living in these four municipalities from the Colombian northeast area is still low.
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Desai K, Druyts E, Yan K, Balijepalli C. On Pandemic Preparedness: How Well is the Modeling Community Prepared for COVID-19? PHARMACOECONOMICS 2020; 38:1149-1151. [PMID: 32924091 PMCID: PMC7487216 DOI: 10.1007/s40273-020-00959-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Affiliation(s)
- Kamal Desai
- Pharmalytics Group, 422 Richards Street, Suite 170, Vancouver, BC, V6B 2Z4, Canada.
| | - Eric Druyts
- Pharmalytics Group, 422 Richards Street, Suite 170, Vancouver, BC, V6B 2Z4, Canada
| | - Kevin Yan
- Pharmalytics Group, 422 Richards Street, Suite 170, Vancouver, BC, V6B 2Z4, Canada
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
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Balmford B, Annan JD, Hargreaves JC, Altoè M, Bateman IJ. Cross-Country Comparisons of Covid-19: Policy, Politics and the Price of Life. ENVIRONMENTAL & RESOURCE ECONOMICS 2020; 76:525-551. [PMID: 32836862 PMCID: PMC7400753 DOI: 10.1007/s10640-020-00466-5 10.1007/s10640-020-00466-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/09/2020] [Indexed: 06/14/2023]
Abstract
Coronavirus has claimed the lives of over half a million people world-wide and this death toll continues to rise rapidly each day. In the absence of a vaccine, non-clinical preventative measures have been implemented as the principal means of limiting deaths. However, these measures have caused unprecedented disruption to daily lives and economic activity. Given this developing crisis, the potential for a second wave of infections and the near certainty of future pandemics, lessons need to be rapidly gleaned from the available data. We address the challenges of cross-country comparisons by allowing for differences in reporting and variation in underlying socio-economic conditions between countries. Our analyses show that, to date, differences in policy interventions have out-weighed socio-economic variation in explaining the range of death rates observed in the data. Our epidemiological models show that across 8 countries a further week long delay in imposing lockdown would likely have cost more than half a million lives. Furthermore, those countries which acted more promptly saved substantially more lives than those that delayed. Linking decisions over the timing of lockdown and consequent deaths to economic data, we reveal the costs that national governments were implicitly prepared to pay to protect their citizens as reflected in the economic activity foregone to save lives. These 'price of life' estimates vary enormously between countries, ranging from as low as around $100,000 (e.g. the UK, US and Italy) to in excess of $1million (e.g. Denmark, Germany, New Zealand and Korea). The lowest estimates are further reduced once we correct for under-reporting of Covid-19 deaths.
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Affiliation(s)
- Ben Balmford
- Department of Economics, Land, Environment Economics and Policy Institute, University of Exeter Business School, Exeter, EX4 4PU UK
| | | | | | - Marina Altoè
- Innovation, Impact and Business, University of Exeter, Exeter, UK
| | - Ian J. Bateman
- Department of Economics, Land, Environment Economics and Policy Institute, University of Exeter Business School, Exeter, EX4 4PU UK
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13
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Balmford B, Annan JD, Hargreaves JC, Altoè M, Bateman IJ. Cross-Country Comparisons of Covid-19: Policy, Politics and the Price of Life. ENVIRONMENTAL & RESOURCE ECONOMICS 2020; 76:525-551. [PMID: 32836862 PMCID: PMC7400753 DOI: 10.1007/s10640-020-00466-5] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/09/2020] [Indexed: 05/07/2023]
Abstract
Coronavirus has claimed the lives of over half a million people world-wide and this death toll continues to rise rapidly each day. In the absence of a vaccine, non-clinical preventative measures have been implemented as the principal means of limiting deaths. However, these measures have caused unprecedented disruption to daily lives and economic activity. Given this developing crisis, the potential for a second wave of infections and the near certainty of future pandemics, lessons need to be rapidly gleaned from the available data. We address the challenges of cross-country comparisons by allowing for differences in reporting and variation in underlying socio-economic conditions between countries. Our analyses show that, to date, differences in policy interventions have out-weighed socio-economic variation in explaining the range of death rates observed in the data. Our epidemiological models show that across 8 countries a further week long delay in imposing lockdown would likely have cost more than half a million lives. Furthermore, those countries which acted more promptly saved substantially more lives than those that delayed. Linking decisions over the timing of lockdown and consequent deaths to economic data, we reveal the costs that national governments were implicitly prepared to pay to protect their citizens as reflected in the economic activity foregone to save lives. These 'price of life' estimates vary enormously between countries, ranging from as low as around $100,000 (e.g. the UK, US and Italy) to in excess of $1million (e.g. Denmark, Germany, New Zealand and Korea). The lowest estimates are further reduced once we correct for under-reporting of Covid-19 deaths.
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Affiliation(s)
- Ben Balmford
- Department of Economics, Land, Environment Economics and Policy Institute, University of Exeter Business School, Exeter, EX4 4PU UK
| | | | | | - Marina Altoè
- Innovation, Impact and Business, University of Exeter, Exeter, UK
| | - Ian J. Bateman
- Department of Economics, Land, Environment Economics and Policy Institute, University of Exeter Business School, Exeter, EX4 4PU UK
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