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Silva M, Viana CM, Betco I, Nogueira P, Roquette R, Rocha J. Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19. Front Public Health 2024; 12:1359167. [PMID: 39022425 PMCID: PMC11251998 DOI: 10.3389/fpubh.2024.1359167] [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: 12/20/2023] [Accepted: 06/17/2024] [Indexed: 07/20/2024] Open
Abstract
Nowadays, epidemiological modeling is applied to a wide range of diseases, communicable and non-communicable, namely AIDS, Ebola, influenza, Dengue, Malaria, Zika. More recently, in the context of the last pandemic declared by the World Health Organization (WHO), several studies applied these models to SARS-CoV-2. Despite the increasing number of researches using spatial analysis, some constraints persist that prevent more complex modeling such as capturing local epidemiological dynamics or capturing the real patterns and dynamics. For example, the unavailability of: (i) epidemiological information such as the frequency with which it is made available; (ii) sociodemographic and environmental factors (e.g., population density and population mobility) at a finer scale which influence the evolution patterns of infectious diseases; or (iii) the number of cases information that is also very dependent on the degree of testing performed, often with severe territorial disparities and influenced by context factors. Moreover, the delay in case reporting and the lack of quality control in epidemiological information is responsible for biases in the data that lead to many results obtained being subject to the ecological fallacy, making it difficult to identify causal relationships. Other important methodological limitations are the control of spatiotemporal dependence, management of non-linearity, ergodicy, among others, which can impute inconsistencies to the results. In addition to these issues, social contact, is still difficult to quantify in order to be incorporated into modeling processes. This study aims to explore a modeling framework that can overcome some of these modeling methodological limitations to allow more accurate modeling of epidemiological diseases. Based on Geographic Information Systems (GIS) and spatial analysis, our model is developed to identify group of municipalities where population density (vulnerability) has a stronger relationship with incidence (hazard) and commuting movements (exposure). Specifically, our framework shows how to operate a model over data with no clear trend or seasonal pattern which is suitable for a short-term predicting (i.e., forecasting) of cases based on few determinants. Our tested models provide a good alternative for when explanatory data is few and the time component is not available, once they have shown a good fit and good short-term forecast ability.
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Affiliation(s)
- Melissa Silva
- Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, Lisbon, Portugal
| | - Cláudia M. Viana
- Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, Lisbon, Portugal
| | - Iuria Betco
- Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, Lisbon, Portugal
| | - Paulo Nogueira
- Associated Laboratory TERRA, Nursing Research, Innovation and Development Centre of Lisbon (CIDNUR), Nursing School of Lisbon, Lisbon, Portugal
- Instituto de Saúde Ambiental (ISAMB), Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
- Escola Nacional de Saúde Pública, ENSP, Centro de Investigação em Saúde Pública, CISP, Comprehensive Health Research Center, CHRC, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Rita Roquette
- NOVA IMS Information Management School, NOVA University of Lisbon, Lisbon, Portugal
| | - Jorge Rocha
- Associated Laboratory TERRA, Institute of Geography and Spatial Planning, University of Lisbon, Lisbon, Portugal
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2
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Barreras F, Watts DJ. The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling. NATURE COMPUTATIONAL SCIENCE 2024; 4:398-411. [PMID: 38898315 DOI: 10.1038/s43588-024-00637-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 05/02/2024] [Indexed: 06/21/2024]
Abstract
Large-scale GPS location datasets hold immense potential for measuring human mobility and interpersonal contact, both of which are essential for data-driven epidemiology. However, despite their potential and widespread adoption during the COVID-19 pandemic, there are several challenges with these data that raise concerns regarding the validity and robustness of its applications. Here we outline two types of challenges-some related to accessing and processing these data, and some related to data quality-and propose several research directions to address them moving forward.
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Affiliation(s)
- Francisco Barreras
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Duncan J Watts
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
- Operations, Information and Decisions Department, Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
- Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, USA.
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3
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Lyu M, Liu K, Hall RW. Spatial Interaction Analysis of Infectious Disease Import and Export between Regions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:643. [PMID: 38791857 PMCID: PMC11120745 DOI: 10.3390/ijerph21050643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/07/2024] [Accepted: 05/12/2024] [Indexed: 05/26/2024]
Abstract
Human travel plays a crucial role in the spread of infectious disease between regions. Travel of infected individuals from one region to another can transport a virus to places that were previously unaffected or may accelerate the spread of disease in places where the disease is not yet well established. We develop and apply models and metrics to analyze the role of inter-regional travel relative to the spread of disease, drawing from data on COVID-19 in the United States. To better understand how transportation affects disease transmission, we established a multi-regional time-varying compartmental disease model with spatial interaction. The compartmental model was integrated with statistical estimates of travel between regions. From the integrated model, we derived a transmission import index to assess the risk of COVID-19 transmission between states. Based on the index, we determined states with high risk for disease spreading to other states at the scale of months, and we analyzed how the index changed over time during 2020. Our model provides a tool for policymakers to evaluate the influence of travel between regions on disease transmission in support of strategies for epidemic control.
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Affiliation(s)
- Mingdong Lyu
- National Renewable Energy Laboratory, Mobility, Behavior, and Advanced Powertrains Department, Denver, CO 80401, USA
| | - Kuofu Liu
- Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089, USA; (K.L.); (R.W.H.)
| | - Randolph W. Hall
- Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089, USA; (K.L.); (R.W.H.)
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4
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Mahmud AS, Martinez PP, Baker RE. The impact of current and future climates on spatiotemporal dynamics of influenza in a tropical setting. PNAS NEXUS 2023; 2:pgad307. [PMID: 38741656 PMCID: PMC11089418 DOI: 10.1093/pnasnexus/pgad307] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 07/25/2023] [Accepted: 09/11/2023] [Indexed: 05/16/2024]
Abstract
Although the drivers of influenza have been well studied in high-income settings in temperate regions, many open questions remain about the burden, seasonality, and drivers of influenza dynamics in the tropics. In temperate climates, the inverse relationship between specific humidity and transmission can explain much of the observed temporal and spatial patterns of influenza outbreaks. Yet, this relationship fails to explain seasonality, or lack there-of, in tropical and subtropical countries. Here, we analyzed eight years of influenza surveillance data from 12 locations in Bangladesh to quantify the role of climate in driving disease dynamics in a tropical setting with a distinct rainy season. We find strong evidence for a nonlinear bimodal relationship between specific humidity and influenza transmission in Bangladesh, with highest transmission occurring for relatively low and high specific humidity values. We simulated influenza burden under current and future climate in Bangladesh using a mathematical model with a bimodal relationship between humidity and transmission, and decreased transmission at very high temperatures, while accounting for changes in population immunity. The climate-driven mechanistic model can accurately capture both the temporal and spatial variation in influenza activity observed across Bangladesh, highlighting the usefulness of mechanistic models for low-income countries with inadequate surveillance. By using climate model projections, we also highlight the potential impact of climate change on influenza dynamics in the tropics and the public health consequences.
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Affiliation(s)
- Ayesha S Mahmud
- Department of Demography, University of California, Berkeley, Berkeley, CA, USA
| | - Pamela P Martinez
- Department of Microbiology, University of Illinois Urbana-Champaign, Champaign, IL, USA
- Department of Statistics, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Rachel E Baker
- Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
- Institute at Brown for Environment and Society, Brown University, Providence, RI, USA
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5
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Gibbs H, Musah A, Seidu O, Ampofo W, Asiedu-Bekoe F, Gray J, Adewole WA, Cheshire J, Marks M, Eggo RM. Call detail record aggregation methodology impacts infectious disease models informed by human mobility. PLoS Comput Biol 2023; 19:e1011368. [PMID: 37561812 PMCID: PMC10443843 DOI: 10.1371/journal.pcbi.1011368] [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: 01/25/2023] [Revised: 08/22/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023] Open
Abstract
This paper demonstrates how two different methods used to calculate population-level mobility from Call Detail Records (CDR) produce varying predictions of the spread of epidemics informed by these data. Our findings are based on one CDR dataset describing inter-district movement in Ghana in 2021, produced using two different aggregation methodologies. One methodology, "all pairs," is designed to retain long distance network connections while the other, "sequential" methodology is designed to accurately reflect the volume of travel between locations. We show how the choice of methodology feeds through models of human mobility to the predictions of a metapopulation SEIR model of disease transmission. We also show that this impact varies depending on the location of pathogen introduction and the transmissibility of infections. For central locations or highly transmissible diseases, we do not observe significant differences between aggregation methodologies on the predicted spread of disease. For less transmissible diseases or those introduced into remote locations, we find that the choice of aggregation methodology influences the speed of spatial spread as well as the size of the peak number of infections in individual districts. Our findings can help researchers and users of epidemiological models to understand how methodological choices at the level of model inputs may influence the results of models of infectious disease transmission, as well as the circumstances in which these choices do not alter model predictions.
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Affiliation(s)
- Hamish Gibbs
- Department of Geography, University College London, London, United Kingdom
| | - Anwar Musah
- Department of Geography, University College London, London, United Kingdom
| | | | - William Ampofo
- Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Accra, Ghana
| | | | | | | | - James Cheshire
- Department of Geography, University College London, London, United Kingdom
| | - Michael Marks
- Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Hospital for Tropical Diseases, University College London Hospital, London, United Kingdom
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Rosalind M. Eggo
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
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6
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Iyaniwura SA, Ringa N, Adu PA, Mak S, Janjua NZ, Irvine MA, Otterstatter M. Understanding the impact of mobility on COVID-19 spread: A hybrid gravity-metapopulation model of COVID-19. PLoS Comput Biol 2023; 19:e1011123. [PMID: 37172027 DOI: 10.1371/journal.pcbi.1011123] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 05/24/2023] [Accepted: 04/24/2023] [Indexed: 05/14/2023] Open
Abstract
The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our modeling framework has the flexibility of determining mobility between regions based on the distances between the regions or using data from mobile devices. In addition, our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in Metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions.
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Affiliation(s)
- Sarafa A Iyaniwura
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Notice Ringa
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Prince A Adu
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sunny Mak
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
| | - Naveed Z Janjua
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Michael A Irvine
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Michael Otterstatter
- British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
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7
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Stenseth NC, Schlatte R, Liu X, Pielke R, Li R, Chen B, Bjørnstad ON, Kusnezov D, Gao GF, Fraser C, Whittington JD, Bai Y, Deng K, Gong P, Guan D, Xiao Y, Xu B, Johnsen EB. How to avoid a local epidemic becoming a global pandemic. Proc Natl Acad Sci U S A 2023; 120:e2220080120. [PMID: 36848570 PMCID: PMC10013804 DOI: 10.1073/pnas.2220080120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 01/10/2023] [Indexed: 03/01/2023] Open
Abstract
Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.
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Affiliation(s)
- Nils Chr. Stenseth
- Center for Pandemics and One Health Research, Sustainable Health Unit (SUSTAINIT), Faculty of Medicine, Oslo0316, Norway
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
| | - Rudolf Schlatte
- Department of Informatics, University of Oslo, Oslo0316, Norway
| | - Xiaoli Liu
- Department of Computer Science, University of Helsinki, 00560Helsinki, Finland
| | - Roger Pielke
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
- Department of Environmental Studies, University of Colorado Boulder, Boulder, CO80309
| | - Ruiyun Li
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
| | - Bin Chen
- Future Urbanity & Sustainable Environment (FUSE) Lab, Division of Landscape Architecture, Faculty of Architecture, University of Hong Kong, Hong Kong999077, China
- Department of Geography, Urban Systems Institute, University of Hong Kong, Hong Kong999077, China
- HKU Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong999077, China
| | - Ottar N. Bjørnstad
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
- Department of Biology, Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA16802
| | - Dimitri Kusnezov
- Deputy Under Secretary, Artificial Intelligence & Technology Office, US Department of Energy, Washington,DC20585
| | - George F. Gao
- Chinese Academy of Sciences Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing100101, China
- Chinese Center for Disease Control and Prevention, Beijing102206, China
| | - Christophe Fraser
- Pandemic Sciences Institute, University of Oxford, OxfordOX3 7DQ, UK
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford0X3 7LFUK
| | - Jason D. Whittington
- Center for Pandemics and One Health Research, Sustainable Health Unit (SUSTAINIT), Faculty of Medicine, Oslo0316, Norway
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo0316, Norway
| | - Yuqi Bai
- Department of Earth System Science, Tsinghua University, Beijing100084, China
- Ministry of Education Ecological Field Station for East Asia Migratory Birds, Tsinghua University, Beijing100084, China
| | - Ke Deng
- Center for Statistical Science, Tsinghua University, Beijing100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing100084, China
| | - Peng Gong
- Department of Earth Sciences, University of Hong Kong, Hong Kong999077, China
- The Bartlett School of Sustainable Construction, University College London, LondonWC1E 6BT, UK
| | - Dabo Guan
- Department of Earth System Science, Tsinghua University, Beijing100084, China
- The Bartlett School of Sustainable Construction, University College London, LondonWC1E 6BT, UK
| | - Yixiong Xiao
- Business Intelligence Lab, Baidu Research, Beijing100193, China
| | - Bing Xu
- Department of Earth System Science, Tsinghua University, Beijing100084, China
- Ministry of Education Ecological Field Station for East Asia Migratory Birds, Tsinghua University, Beijing100084, China
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8
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Galiwango R, Bainomugisha E, Kivunike F, Kateete DP, Jjingo D. Air pollution and mobility patterns in two Ugandan cities during COVID-19 mobility restrictions suggest the validity of air quality data as a measure for human mobility. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:34856-34871. [PMID: 36520281 PMCID: PMC9751517 DOI: 10.1007/s11356-022-24605-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
We explored the viability of using air quality as an alternative to aggregated location data from mobile phones in the two most populated cities in Uganda. We accessed air quality and Google mobility data collected from 15th February 2020 to 10th June 2021 and augmented them with mobility restrictions implemented during the COVID-19 lockdown. We determined whether air quality data depicted similar patterns to mobility data before, during, and after the lockdown and determined associations between air quality and mobility by computing Pearson correlation coefficients ([Formula: see text]), conducting multivariable regression with associated confidence intervals (CIs), and visualized the relationships using scatter plots. Residential mobility increased with the stringency of restrictions while both non-residential mobility and air pollution decreased with the stringency of restrictions. In Kampala, PM2.5 was positively correlated with non-residential mobility and negatively correlated with residential mobility. Only correlations between PM2.5 and movement in work and residential places were statistically significant in Wakiso. After controlling for stringency in restrictions, air quality in Kampala was independently correlated with movement in retail and recreation (- 0.55; 95% CI = - 1.01- - 0.10), parks (0.29; 95% CI = 0.03-0.54), transit stations (0.29; 95% CI = 0.16-0.42), work (- 0.25; 95% CI = - 0.43- - 0.08), and residential places (- 1.02; 95% CI = - 1.4- - 0.64). For Wakiso, only the correlation between air quality and residential mobility was statistically significant (- 0.99; 95% CI = - 1.34- - 0.65). These findings suggest that air quality is linked to mobility and thus could be used by public health programs in monitoring movement patterns and the spread of infectious diseases without compromising on individuals' privacy.
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Affiliation(s)
- Ronald Galiwango
- The African Center of Excellence in Bioinformatics and Data Intensive Sciences, The Infectious Diseases Institute, Makerere University, Kampala, Uganda.
- Center for Computational Biology, Uganda Christian University, Mukono, Uganda.
| | - Engineer Bainomugisha
- Department of Computer Science, College of Computing and Information Sciences, Makerere University, Kampala, Uganda
| | - Florence Kivunike
- Department of Computer Science, College of Computing and Information Sciences, Makerere University, Kampala, Uganda
| | - David Patrick Kateete
- Department of Immunology and Molecular Biology, College of Health Sciences, Makerere University, Kampala, Uganda
- Department of Medical Microbiology, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Daudi Jjingo
- The African Center of Excellence in Bioinformatics and Data Intensive Sciences, The Infectious Diseases Institute, Makerere University, Kampala, Uganda
- Department of Computer Science, College of Computing and Information Sciences, Makerere University, Kampala, Uganda
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9
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Engebretsen S, Diz-Lois Palomares A, Rø G, Kristoffersen AB, Lindstrøm JC, Engø-Monsen K, Kamineni M, Hin Chan LY, Dale Ø, Midtbø JE, Stenerud KL, Di Ruscio F, White R, Frigessi A, de Blasio BF. A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting. PLoS Comput Biol 2023; 19:e1010860. [PMID: 36689468 PMCID: PMC9894546 DOI: 10.1371/journal.pcbi.1010860] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 02/02/2023] [Accepted: 01/08/2023] [Indexed: 01/24/2023] Open
Abstract
The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.
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Affiliation(s)
| | | | - Gunnar Rø
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | | | | | | | - Meghana Kamineni
- Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Louis Yat Hin Chan
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | | | - Jørgen Eriksson Midtbø
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
- Telenor Norge AS Fornebu, Norway
| | | | - Francesco Di Ruscio
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | - Richard White
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway
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10
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Brown TS, Robinson DA, Buckee CO, Mathema B. Connecting the dots: understanding how human mobility shapes TB epidemics. Trends Microbiol 2022; 30:1036-1044. [PMID: 35597716 PMCID: PMC10068677 DOI: 10.1016/j.tim.2022.04.005] [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: 01/10/2022] [Revised: 04/18/2022] [Accepted: 04/19/2022] [Indexed: 01/13/2023]
Abstract
Tuberculosis (TB) remains a leading infectious cause of death worldwide. Reducing TB infections and TB-related deaths rests ultimately on stopping forward transmission from infectious to susceptible individuals. Critical to this effort is understanding how human host mobility shapes the transmission and dispersal of new or existing strains of Mycobacterium tuberculosis (Mtb). Important questions remain unanswered. What kinds of mobility, over what temporal and spatial scales, facilitate TB transmission? How do human mobility patterns influence the dispersal of novel Mtb strains, including emergent drug-resistant strains? This review summarizes the current state of knowledge on mobility and TB epidemic dynamics, using examples from three topic areas, including inference of genetic and spatial clustering of infections, delineating source-sink dynamics, and mapping the dispersal of novel TB strains, to examine scientific questions and methodological issues within this topic. We also review new data sources for measuring human mobility, including mobile phone-associated movement data, and discuss important limitations on their use in TB epidemiology.
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Affiliation(s)
- Tyler S Brown
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Infectious Diseases Division, Massachusetts General Hospital, Boston, MA, USA
| | - D Ashley Robinson
- Department of Microbiology and Immunology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Barun Mathema
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
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11
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Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data. DATA 2022. [DOI: 10.3390/data7080107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this paper captures the association between changes in mobility patterns through time and the corresponding COVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate a strong relationship between mobility data and COVID-19 incidence, suggesting that more mobility is associated with more COVID-19 cases. Methodological procedures can be summarized in a multiple linear regression with a time moving window. Model validation demonstrate good forecast accuracy, particularly when we consider the cumulative number of cases. Based on this premise, it is possible to estimate and predict future evolution of the number of COVID-19 cases using near real-time information of population mobility.
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Berry I, Rahman M, Flora MS, Shirin T, Alamgir ASM, Khan MH, Anwar R, Lisa M, Chowdhury F, Islam MA, Osmani MG, Dunkle S, Brum E, Greer AL, Morris SK, Mangtani P, Fisman DN. Seasonality of influenza and coseasonality with avian influenza in Bangladesh, 2010–19: a retrospective, time-series analysis. Lancet Glob Health 2022; 10:e1150-e1158. [DOI: 10.1016/s2214-109x(22)00212-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/22/2022] [Accepted: 04/11/2022] [Indexed: 10/18/2022]
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Hasan A, Sasaki T, Phadungsombat J, Koketsu R, Rahim R, Ara N, Biswas SM, Yonezawa R, Nakayama EE, Rahman M, Shioda T. Genetic Analysis of Influenza A/H1N1pdm Strains Isolated in Bangladesh in Early 2020. Trop Med Infect Dis 2022; 7:tropicalmed7030038. [PMID: 35324585 PMCID: PMC8949303 DOI: 10.3390/tropicalmed7030038] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/24/2022] [Accepted: 02/26/2022] [Indexed: 12/10/2022] Open
Abstract
Influenza is one of the most common respiratory virus infections. We analyzed hemagglutinin (HA) and neuraminidase (NA) gene segments of viruses isolated from influenza patients who visited Evercare Hospital Dhaka, Bangladesh, in early 2020 immediately before the coronavirus disease 2019 (COVID-19) pandemic. All of them were influenza virus type A (IAV) H1N1pdm. Sequence analysis of the HA segments of the virus strains isolated from the clinical specimens and the subsequent phylogenic analyses of the obtained sequences revealed that all of the H1N1pdm recent subclades 6B.1A5A + 187V/A, 6B.1A5A + 156K, and 6B.1A5A + 156K with K209M were already present in Bangladesh in January 2020. Molecular clock analysis results suggested that the subclade 6B.1A5A + 156K emerged in Denmark, Australia, or the United States in July 2019, while subclades 6B.1A5A + 187V/A and 6B.1A5A + 156K with K209M emerged in East Asia in April and September 2019, respectively. On the other hand, sequence analysis of NA segments showed that the viruses lacked the H275Y mutation that confers oseltamivir resistance. Since the number of influenza cases in Bangladesh is usually small between November and January, these results indicated that the IAV H1N1pdm had spread extremely rapidly without acquiring oseltamivir resistance during a time of active international flow of people before the COVID-19 pandemic.
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Affiliation(s)
- Abu Hasan
- Evercare Hospital Dhaka (Ex Apollo Hospitals Dhaka), Dhaka 1229, Bangladesh; (A.H.); (R.R.); (N.A.); (S.M.B.)
| | - Tadahiro Sasaki
- Research Institute of Microbial Diseases, Osaka University, Suita 565-0781, Japan; (T.S.); (J.P.); (R.K.); (R.Y.); (E.E.N.)
- Center for Infectious Disease Education and Research, Osaka University, Suita 565-0781, Japan
| | - Juthamas Phadungsombat
- Research Institute of Microbial Diseases, Osaka University, Suita 565-0781, Japan; (T.S.); (J.P.); (R.K.); (R.Y.); (E.E.N.)
- Center for Infectious Disease Education and Research, Osaka University, Suita 565-0781, Japan
- Mahidol-Osaka Center for Infectious Diseases (MOCID), Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand
| | - Ritsuko Koketsu
- Research Institute of Microbial Diseases, Osaka University, Suita 565-0781, Japan; (T.S.); (J.P.); (R.K.); (R.Y.); (E.E.N.)
- Center for Infectious Disease Education and Research, Osaka University, Suita 565-0781, Japan
| | - Rummana Rahim
- Evercare Hospital Dhaka (Ex Apollo Hospitals Dhaka), Dhaka 1229, Bangladesh; (A.H.); (R.R.); (N.A.); (S.M.B.)
| | - Nikhat Ara
- Evercare Hospital Dhaka (Ex Apollo Hospitals Dhaka), Dhaka 1229, Bangladesh; (A.H.); (R.R.); (N.A.); (S.M.B.)
| | - Suma Mita Biswas
- Evercare Hospital Dhaka (Ex Apollo Hospitals Dhaka), Dhaka 1229, Bangladesh; (A.H.); (R.R.); (N.A.); (S.M.B.)
| | - Riku Yonezawa
- Research Institute of Microbial Diseases, Osaka University, Suita 565-0781, Japan; (T.S.); (J.P.); (R.K.); (R.Y.); (E.E.N.)
- Center for Infectious Disease Education and Research, Osaka University, Suita 565-0781, Japan
| | - Emi E. Nakayama
- Research Institute of Microbial Diseases, Osaka University, Suita 565-0781, Japan; (T.S.); (J.P.); (R.K.); (R.Y.); (E.E.N.)
- Center for Infectious Disease Education and Research, Osaka University, Suita 565-0781, Japan
| | - Mizanur Rahman
- Evercare Hospital Dhaka (Ex Apollo Hospitals Dhaka), Dhaka 1229, Bangladesh; (A.H.); (R.R.); (N.A.); (S.M.B.)
- Correspondence: (M.R.); (T.S.)
| | - Tatsuo Shioda
- Research Institute of Microbial Diseases, Osaka University, Suita 565-0781, Japan; (T.S.); (J.P.); (R.K.); (R.Y.); (E.E.N.)
- Center for Infectious Disease Education and Research, Osaka University, Suita 565-0781, Japan
- Mahidol-Osaka Center for Infectious Diseases (MOCID), Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand
- Correspondence: (M.R.); (T.S.)
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