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Wang P, Huang J. A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China. PLoS One 2023; 18:e0293803. [PMID: 37948384 PMCID: PMC10637684 DOI: 10.1371/journal.pone.0293803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 10/19/2023] [Indexed: 11/12/2023] Open
Abstract
The outbreak of the Coronavirus Disease 2019 (COVID-19) has profoundly influenced daily life, necessitating the understanding of the relationship between the epidemic's progression and population dynamics. In this study, we present a data-driven framework that integrates GIS-based data mining technology and a Susceptible, Exposed, Infected and Recovered (SEIR) model. This approach helps delineate population dynamics at the grid and community scales and analyze the impacts of government policies, urban functional areas, and intercity flows on population dynamics during the pandemic. Xiamen Island was selected as a case study to validate the effectiveness of the data-driven framework. The results of the high/low cluster analysis provide 99% certainty (P < 0.01) that the population distribution between January 23 and March 16, 2020, was not random, a phenomenon referred to as high-value clustering. The SEIR model predicts that a ten-day delay in implementing a lockdown policy during an epidemic can lead to a significant increase in the number of individuals infected by the virus. Throughout the epidemic prevention and control period (January 23 to February 21, 2020), residential and transportation areas housed more residents. After the resumption of regular activities, the population was mainly concentrated in residential, industrial, and transportation, as well as road facility areas. Notably, the migration patterns into and out of Xiamen were primarily centered on neighboring cities both before and after the outbreak. However, migration indices from cities outside the affected province drastically decreased and approached zero following the COVID-19 outbreak. Our findings offer new insights into the interplay between the epidemic's development and population dynamics, which enhances the prevention and control of the coronavirus epidemic.
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Affiliation(s)
- Peng Wang
- Fujian Key Laboratory of Coastal Pollution Prevention and Control, Xiamen University, Xiamen, China
| | - Jinliang Huang
- Fujian Key Laboratory of Coastal Pollution Prevention and Control, Xiamen University, Xiamen, China
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2
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Nguyen MH, Nguyen THT, Molenberghs G, Abrams S, Hens N, Faes C. The impact of national and international travel on spatio-temporal transmission of SARS-CoV-2 in Belgium in 2021. BMC Infect Dis 2023; 23:428. [PMID: 37355572 DOI: 10.1186/s12879-023-08368-9] [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: 11/17/2022] [Accepted: 06/02/2023] [Indexed: 06/26/2023] Open
Abstract
BACKGROUND The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread over the world and caused tremendous impacts on global health. Understanding the mechanism responsible for the spread of this pathogen and the impact of specific factors, such as human mobility, will help authorities to tailor interventions for future SARS-CoV-2 waves or newly emerging airborne infections. In this study, we aim to analyze the spatio-temporal transmission of SARS-CoV-2 in Belgium at municipality level between January and December 2021 and explore the effect of different levels of human travel on disease incidence through the use of counterfactual scenarios. METHODS We applied the endemic-epidemic modelling framework, in which the disease incidence decomposes into endemic, autoregressive and neighbourhood components. The spatial dependencies among areas are adjusted based on actual connectivity through mobile network data. We also took into account other important factors such as international mobility, vaccination coverage, population size and the stringency of restriction measures. RESULTS The results demonstrate the aggravating effect of international travel on the incidence, and simulated counterfactual scenarios further stress the alleviating impact of a reduction in national and international travel on epidemic growth. It is also clear that local transmission contributed the most during 2021, and municipalities with a larger population tended to attract a higher number of cases from neighboring areas. CONCLUSIONS Although transmission between municipalities was observed, local transmission was dominant. We highlight the positive association between the mobility data and the infection spread over time. Our study provides insight to assist health authorities in decision-making, particularly when the disease is airborne and therefore likely influenced by human movement.
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Affiliation(s)
- Minh Hanh Nguyen
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium.
| | | | - Geert Molenberghs
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- I-BioStat, Katholieke Universiteit Leuven, BE-3000, Leuven, Belgium
| | - Steven Abrams
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- Global Health Institute, University of Antwerp, BE-2000, Antwerpen, Belgium
| | - Niel Hens
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- Global Health Institute, University of Antwerp, BE-2000, Antwerpen, Belgium
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, BE-2000, Antwerpen, Belgium
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- I-BioStat, Katholieke Universiteit Leuven, BE-3000, Leuven, Belgium
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3
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Hong Z, Li Y, Gong Y, Chen W. A data-driven spatially-specific vaccine allocation framework for COVID-19. ANNALS OF OPERATIONS RESEARCH 2022:1-24. [PMID: 36467001 PMCID: PMC9684883 DOI: 10.1007/s10479-022-05037-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/18/2022] [Indexed: 05/30/2023]
Abstract
Although coronavirus disease 2019 (COVID-19) vaccines have been introduced, their allocation is a challenging problem. We propose a data-driven, spatially-specific vaccine allocation framework that aims to minimize the number of COVID-19-related deaths or infections. The framework combines a regional risk-level classification model solved by a self-organizing map neural network, a spatially-specific disease progression model, and a vaccine allocation model that considers vaccine production capacity. We use data obtained from Wuhan and 35 other cities in China from January 26 to February 11, 2020, to avoid the effects of intervention. Our results suggest that, in region-wise distribution of vaccines, they should be allocated first to the source region of the outbreak and then to the other regions in order of decreasing risk whether the outcome measure is the number of deaths or infections. This spatially-specific vaccine allocation policy significantly outperforms some current allocation policies.
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Affiliation(s)
- Zhaofu Hong
- School of Management, Northwestern Polytechnical University, Xi’an, People’s Republic of China
| | - Yingjie Li
- School of Civil Engineering, Central South University, Changsha, People’s Republic of China
- School of Management, Lanzhou University, Lanzhou, People’s Republic of China
| | | | - Wanying Chen
- School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, People’s Republic of China
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Vaziry A, Kolokolnikov T, Kevrekidis PG. Modelling of spatial infection spread through heterogeneous population: from lattice to partial differential equation models. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220064. [PMID: 36249333 PMCID: PMC9533003 DOI: 10.1098/rsos.220064] [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: 01/21/2022] [Accepted: 09/05/2022] [Indexed: 06/16/2023]
Abstract
We present a simple model for the spread of an infection that incorporates spatial variability in population density. Starting from first-principle considerations, we explore how a novel partial differential equation with state-dependent diffusion can be obtained. This model exhibits higher infection rates in the areas of higher population density-a feature that we argue to be consistent with epidemiological observations. The model also exhibits an infection wave, the speed of which varies with population density. In addition, we demonstrate the possibility that an infection can 'jump' (i.e. tunnel) across areas of low population density towards areas of high population density. We briefly touch upon the data reported for coronavirus spread in the Canadian province of Nova Scotia as a case example with a number of qualitatively similar features as our model. Lastly, we propose a number of generalizations of the model towards future studies.
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Affiliation(s)
- Arvin Vaziry
- Department of Mathematics and Statistics, Dalhousie University Halifax, Nova Scotia, Canada B3H3J5
| | - T. Kolokolnikov
- Department of Mathematics and Statistics, Dalhousie University Halifax, Nova Scotia, Canada B3H3J5
| | - P. G. Kevrekidis
- Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003-4515, USA
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5
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Zhang W, Liu S, Osgood N, Zhu H, Qian Y, Jia P. Using simulation modelling and systems science to help contain COVID-19: A systematic review. SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE 2022; 40:SRES2897. [PMID: 36245570 PMCID: PMC9538520 DOI: 10.1002/sres.2897] [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: 10/27/2021] [Revised: 05/23/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.
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Affiliation(s)
- Weiwei Zhang
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Shiyong Liu
- Institute of Advanced Studies in Humanities and Social SciencesBeijing Normal University at ZhuhaiZhuhaiChina
| | - Nathaniel Osgood
- Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
- Department of Community Health and EpidemiologyUniversity of SaskatchewanSaskatoonCanada
| | - Hongli Zhu
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Ying Qian
- Business SchoolUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Peng Jia
- School of Resource and Environmental SciencesWuhan UniversityWuhanHubeiChina
- International Institute of Spatial Lifecourse HealthWuhan UniversityWuhanHubeiChina
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6
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Bilgel F, Karahasan BC. Effects of Vaccination and the Spatio-Temporal Diffusion of Covid-19 Incidence in Turkey. GEOGRAPHICAL ANALYSIS 2022; 55:GEAN12335. [PMID: 36118737 PMCID: PMC9467643 DOI: 10.1111/gean.12335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 04/04/2022] [Accepted: 04/27/2022] [Indexed: 06/15/2023]
Abstract
This study assesses the spatio-temporal impact of vaccination efforts on Covid-19 incidence growth in Turkey. Incorporating geographical features of SARS-CoV-2 transmission, we adopt a spatial Susceptible-Infected-Recovered (SIR) model that serves as a guide of our empirical specification. Using provincial weekly panel data, we estimate a dynamic spatial autoregressive (SAR) model to elucidate the short- and the long-run impact of vaccination on Covid-19 incidence growth after controlling for temporal and spatio-temporal diffusion, testing capacity, social distancing behavior and unobserved space-varying confounders. Results show that vaccination growth reduces Covid-19 incidence growth rate directly and indirectly by creating a positive externality over space. The significant association between vaccination and Covid-19 incidence is robust to a host of spatial weight matrix specifications. Conspicuous spatial and temporal diffusion effects of Covid-19 incidence growth were found across all specifications: the former being a severer threat to the containment of the pandemic than the latter.
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Affiliation(s)
- Firat Bilgel
- Department of EconomicsMEF UniversityIstanbul34396Turkey
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7
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Slater JJ, Brown PE, Rosenthal JS, Mateu J. Capturing spatial dependence of COVID-19 case counts with cellphone mobility data. SPATIAL STATISTICS 2022; 49:100540. [PMID: 34603946 PMCID: PMC8479517 DOI: 10.1016/j.spasta.2021.100540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 08/26/2021] [Accepted: 09/06/2021] [Indexed: 05/07/2023]
Abstract
Spatial dependence is usually introduced into spatial models using some measure of physical proximity. When analysing COVID-19 case counts, this makes sense as regions that are close together are more likely to have more people moving between them, spreading the disease. However, using the actual number of trips between each region may explain COVID-19 case counts better than physical proximity. In this paper, we investigate the efficacy of using telecommunications-derived mobility data to induce spatial dependence in spatial models applied to two Spanish communities' COVID-19 case counts. We do this by extending Besag York Mollié (BYM) models to include both a physical adjacency effect, alongside a mobility effect. The mobility effect is given a Gaussian Markov random field prior, with the number of trips between regions as edge weights. We leverage modern parametrizations of BYM models to conclude that the number of people moving between regions better explains variation in COVID-19 case counts than physical proximity data. We suggest that this data should be used in conjunction with physical proximity data when developing spatial models for COVID-19 case counts.
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Affiliation(s)
- Justin J Slater
- Department of Statistical Sciences, University of Toronto, Canada
- Centre for Global Health Research, St. Michael's Hospital, Canada
| | - Patrick E Brown
- Department of Statistical Sciences, University of Toronto, Canada
- Centre for Global Health Research, St. Michael's Hospital, Canada
| | | | - Jorge Mateu
- Department of Mathematics, University Jaume I of Castellon, Spain
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8
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D'Urso P, De Giovanni L, Vitale V. A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy. SPATIAL STATISTICS 2022; 47:100586. [PMID: 35036295 PMCID: PMC8744361 DOI: 10.1016/j.spasta.2021.100586] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/25/2021] [Accepted: 12/31/2021] [Indexed: 05/12/2023]
Abstract
The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some covariates of interest accounting for medical, environmental and demographic factors. To deal with the issue of spatial autocorrelation, the D-vine copula based quantile regression also embeds a spatial autoregressive component that controls for the extent of spatial dependence. The use of vine copula enhances model flexibility accounting for non-linear relationships and tail dependencies. Moreover, the model selection procedure leads to parsimonious models providing a rank of covariates based on their explanatory power with respect to the outcome.
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Affiliation(s)
- Pierpaolo D'Urso
- Department of Social and Economic Sciences, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Livia De Giovanni
- Department of Political Sciences, LUISS University, Viale Romania, 32, 00197 Rome, Italy
| | - Vincenzina Vitale
- Department of Social and Economic Sciences, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
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9
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Ferreira CP, Marcondes D, Melo MP, Oliva SM, Peixoto CM, Peixoto PS. A snapshot of a pandemic: The interplay between social isolation and COVID-19 dynamics in Brazil. PATTERNS (NEW YORK, N.Y.) 2021; 2:100349. [PMID: 34541563 PMCID: PMC8442254 DOI: 10.1016/j.patter.2021.100349] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/13/2021] [Accepted: 08/20/2021] [Indexed: 06/13/2023]
Abstract
In response to the coronavirus pandemic, governments implemented social distancing, attempting to block the virus spread within territories. While it is well accepted that social isolation plays a role in epidemic control, the precise connections between mobility data indicators and epidemic dynamics are still a challenge. In this work, we investigate the dependency between a social isolation index and epidemiological metrics for several Brazilian cities. Classic statistical methods are employed to support the findings. As a first, initially surprising, result, we illustrate how there seems to be no apparent functional relationship between social isolation data and later effects on disease incidence. However, further investigations identified two regimes of successful employment of social isolation: as a preventive measure or as a remedy, albeit remedy measures require greater social isolation and bring higher burden to health systems. Additionally, we exhibit cases of successful strategies involving lockdowns and an indicator-based mobility restriction plan.
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Affiliation(s)
- Cláudia P. Ferreira
- Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, Brazil
| | - Diego Marcondes
- Department of Applied Mathematics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo 05508-090, Brazil
| | - Mariana P. Melo
- Department of Basic and Environmental Sciences, Engineering School of Lorena, University of São Paulo, Lorena 12602-810, Brazil
| | - Sérgio M. Oliva
- Department of Applied Mathematics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo 05508-090, Brazil
| | - Cláudia M. Peixoto
- Department of Applied Mathematics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo 05508-090, Brazil
| | - Pedro S. Peixoto
- Department of Applied Mathematics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo 05508-090, Brazil
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10
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Kevrekidis PG, Cuevas-Maraver J, Drossinos Y, Rapti Z, Kevrekidis GA. Reaction-diffusion spatial modeling of COVID-19: Greece and Andalusia as case examples. Phys Rev E 2021; 104:024412. [PMID: 34525669 DOI: 10.1103/physreve.104.024412] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/02/2021] [Indexed: 01/12/2023]
Abstract
We examine the spatial modeling of the outbreak of COVID-19 in two regions: the autonomous community of Andalusia in Spain and the mainland of Greece. We start with a zero-dimensional (0D; ordinary-differential-equation-level) compartmental epidemiological model consisting of Susceptible, Exposed, Asymptomatic, (symptomatically) Infected, Hospitalized, Recovered, and deceased populations (SEAIHR model). We emphasize the importance of the viral latent period (reflected in the exposed population) and the key role of an asymptomatic population. We optimize model parameters for both regions by comparing predictions to the cumulative number of infected and total number of deaths, the reported data we found to be most reliable, via minimizing the ℓ^{2} norm of the difference between predictions and observed data. We consider the sensitivity of model predictions on reasonable variations of model parameters and initial conditions, and we address issues of parameter identifiability. We model both the prequarantine and postquarantine evolution of the epidemic by a time-dependent change of the viral transmission rates that arises in response to containment measures. Subsequently, a spatially distributed version of the 0D model in the form of reaction-diffusion equations is developed. We consider that, after an initial localized seeding of the infection, its spread is governed by the diffusion (and 0D model "reactions") of the asymptomatic and symptomatically infected populations, which decrease with the imposed restrictive measures. We inserted the maps of the two regions, and we imported population-density data into the finite-element software package COMSOL Multiphysics®, which was subsequently used to numerically solve the model partial differential equations. Upon discussing how to adapt the 0D model to this spatial setting, we show that these models bear significant potential towards capturing both the well-mixed, zero-dimensional description and the spatial expansion of the pandemic in the two regions. Veins of potential refinement of the model assumptions towards future work are also explored.
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Affiliation(s)
- P G Kevrekidis
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts 01003-4515, USA and Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - J Cuevas-Maraver
- Grupo de Física No Lineal, Departamento de Física Aplicada I, Universidad de Sevilla. Escuela Politécnica Superior, C/ Virgen de Africa, 7, 41011-Sevilla, Spain and Instituto de Matemáticas de la Universidad de Sevilla (IMUS). Edificio Celestino Mutis. Avda. Reina Mercedes s/n, 41012-Sevilla, Spain
| | - Y Drossinos
- European Commission, Joint Research Centre, I-21027 Ispra (VA), Italy
| | - Z Rapti
- Department of Mathematics and Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - G A Kevrekidis
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts 01003-4515, USA
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11
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Grave M, Viguerie A, Barros GF, Reali A, Coutinho ALGA. Assessing the Spatio-temporal Spread of COVID-19 via Compartmental Models with Diffusion in Italy, USA, and Brazil. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2021; 28:4205-4223. [PMID: 34335018 PMCID: PMC8315263 DOI: 10.1007/s11831-021-09627-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 06/02/2021] [Indexed: 05/07/2023]
Abstract
The outbreak of COVID-19 in 2020 has led to a surge in interest in the mathematical modeling of infectious diseases. Such models are usually defined as compartmental models, in which the population under study is divided into compartments based on qualitative characteristics, with different assumptions about the nature and rate of transfer across compartments. Though most commonly formulated as ordinary differential equation models, in which the compartments depend only on time, recent works have also focused on partial differential equation (PDE) models, incorporating the variation of an epidemic in space. Such research on PDE models within a Susceptible, Infected, Exposed, Recovered, and Deceased framework has led to promising results in reproducing COVID-19 contagion dynamics. In this paper, we assess the robustness of this modeling framework by considering different geometries over more extended periods than in other similar studies. We first validate our code by reproducing previously shown results for Lombardy, Italy. We then focus on the U.S. state of Georgia and on the Brazilian state of Rio de Janeiro, one of the most impacted areas in the world. Our results show good agreement with real-world epidemiological data in both time and space for all regions across major areas and across three different continents, suggesting that the modeling approach is both valid and robust.
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Affiliation(s)
- Malú Grave
- Department of Civil Engineering, COPPE/Federal University of Rio de Janeiro, P.O. Box 68506, Rio de Janeiro, RJ 21945-970 Brazil
| | - Alex Viguerie
- Department of Mathematics, Gran Sasso Science Institute, Viale Francesco Crispi 7, 67100 L’Aquila, AQ Italy
| | - Gabriel F. Barros
- Department of Civil Engineering, COPPE/Federal University of Rio de Janeiro, P.O. Box 68506, Rio de Janeiro, RJ 21945-970 Brazil
| | - Alessandro Reali
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, PV Italy
| | - Alvaro L. G. A. Coutinho
- Department of Civil Engineering, COPPE/Federal University of Rio de Janeiro, P.O. Box 68506, Rio de Janeiro, RJ 21945-970 Brazil
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A Review of Spatiotemporal Models for Count Data in R Packages. A Case Study of COVID-19 Data. MATHEMATICS 2021. [DOI: 10.3390/math9131538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Spatiotemporal models for count data are required in a wide range of scientific fields, and they have become particularly crucial today because of their ability to analyze COVID-19-related data. The main objective of this paper is to present a review describing the most important approaches, and we monitor their performance under the same dataset. For this review, we focus on the three R-packages that can be used for this purpose, and the different models assessed are representative of the two most widespread methodologies used to analyze spatiotemporal count data: the classical approach and the Bayesian point of view. A COVID-19-related case study is analyzed as an illustration of these different methodologies. Because of the current urgent need for monitoring and predicting data in the COVID-19 pandemic, this case study is, in itself, of particular importance and can be considered the secondary objective of this work. Satisfactory and promising results have been obtained in this second goal. With respect to the main objective, it has been seen that, although the three models provide similar results in our case study, their different properties and flexibility allow us to choose the model depending on the application at hand.
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Abstract
In this paper, we use an SIRD model to analyze the evolution of the COVID-19 pandemic in Spain, caused by a new virus called SARS-CoV-2 from the coronavirus family. This model is governed by a nonlinear system of differential equations that allows us to detect trends in the pandemic and make reliable predictions of the evolution of the infection in the short term. This work shows this evolution of the infection in various changing stages throughout the period of maximum alert in Spain. It also shows a quick adaptation of the parameters that define the disease in several stages. In addition, the model confirms the effectiveness of quarantine to avoid the exponential expansion of the pandemic and reduce the number of deaths. The analysis shows good short-term predictions using the SIRD model, which are useful to influence the evolution of the epidemic and thus carry out actions that help reduce its harmful effects.
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Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases. INFORMATION 2021. [DOI: 10.3390/info12030109] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.
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Kamrujjaman M, Mahmud MS, Ahmed S, Qayum MO, Alam MM, Hassan MN, Islam MR, Nipa KF, Bulut U. SARS-CoV-2 and Rohingya Refugee Camp, Bangladesh: Uncertainty and How the Government Took Over the Situation. BIOLOGY 2021; 10:124. [PMID: 33562509 PMCID: PMC7914953 DOI: 10.3390/biology10020124] [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: 12/23/2020] [Revised: 01/21/2021] [Accepted: 01/29/2021] [Indexed: 12/13/2022]
Abstract
Background: Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, and social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting of the COVID-19 burden is vital in this regard. In this study, we aimed to forecast the COVID-19 obligation among the Rohingya refugees of Bangladesh to keep up with the disease outbreak's pace, health needs, and disaster preparedness. Methodology and Findings: To estimate the possible consequences of COVID-19 in the Rohingya camps of Bangladesh, we used a modified Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model. All of the values of different parameters used in this model were from the Bangladesh Government's database and the relevant emerging literature. We addressed two different scenarios, i.e., the best-fitting model and the good-fitting model with unique consequences of COVID-19. Our best fitting model suggests that there will be reasonable control over the transmission of the COVID-19 disease. At the end of December 2020, there will be only 169 confirmed COVID-19 cases in the Rohingya refugee camps. The average basic reproduction number (R0) has been estimated to be 0.7563. Conclusions: Our analysis suggests that, due to the extensive precautions from the Bangladesh government and other humanitarian organizations, the coronavirus disease will be under control if the maintenance continues like this. However, detailed and pragmatic preparedness should be adopted for the worst scenario.
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Affiliation(s)
- Md. Kamrujjaman
- Department of Mathematics, University of Dhaka, Dhaka 1000, Bangladesh
- Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Md. Shahriar Mahmud
- Department of Computer Science & Engineering, State University of Bangladesh, Dhaka 1205, Bangladesh;
| | - Shakil Ahmed
- Department of Public Health, North South University, Dhaka 1229, Bangladesh;
| | - Md. Omar Qayum
- Institute of Epidemiology, Disease Control and Research, Dhaka 1212, Bangladesh;
| | - Mohammad Morshad Alam
- Health, Nutrition and Population Global Practice, The World Bank, Dhaka 1207, Bangladesh;
| | - Md Nazmul Hassan
- Department of Mathematics, Schreiner University, Kerrville, TX 78028, USA;
| | - Md Rafiul Islam
- Department of Mathematics, Iowa State University, Ames, IA 50011, USA;
| | - Kaniz Fatema Nipa
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA;
| | - Ummugul Bulut
- Department of Mathematical, Physical, and Engineering Sciences, Texas A&M University San Antonio, San Antonio, TX 78224, USA;
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Tat Dat T, Frédéric P, Hang NTT, Jules M, Duc Thang N, Piffault C, Willy R, Susely F, Lê HV, Tuschmann W, Tien Zung N. Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19. BIOLOGY 2020; 9:E477. [PMID: 33353045 PMCID: PMC7767158 DOI: 10.3390/biology9120477] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 12/13/2020] [Accepted: 12/15/2020] [Indexed: 01/27/2023]
Abstract
We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number I(t) of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning-based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the Johns Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida.
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Affiliation(s)
- Tô Tat Dat
- Centre de Mathématiques Laurent-Schwartz, École Polytechnique Cour Vaneau, 91120 Palaiseau, France
| | - Protin Frédéric
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Nguyen T. T. Hang
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Martel Jules
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Nguyen Duc Thang
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Charles Piffault
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Rodríguez Willy
- Ecole Nationale de l’Aviation Civile, 7 Avenue Edouard Belin, 31400 Toulouse, France;
| | - Figueroa Susely
- Torus Actions SAS, 3 Avenue Didier Daurat, 31400 Toulouse, France; (P.F.); (N.T.T.H.); (M.J.); (N.D.T.); (C.P.); (F.S.)
| | - Hông Vân Lê
- Institute of Mathematics of the Czech Academy of Sciences, Zitna 25, 11567 Praha 1, Czech Republic;
| | - Wilderich Tuschmann
- Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), Englerstr. 2, D-76131 Karlsruhe, Germany;
| | - Nguyen Tien Zung
- Institut de Mathematiques de Toulouse, Université Toulouse 3, 18 Route de Narbonne, 31400 Toulouse, France;
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