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Ugwu OPC, Alum EU, Ugwu JN, Eze VHU, Ugwu CN, Ogenyi FC, Okon MB. Harnessing technology for infectious disease response in conflict zones: Challenges, innovations, and policy implications. Medicine (Baltimore) 2024; 103:e38834. [PMID: 38996110 PMCID: PMC11245197 DOI: 10.1097/md.0000000000038834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 06/14/2024] [Indexed: 07/14/2024] Open
Abstract
Epidemic outbreaks of infectious diseases in conflict zones are complex threats to public health and humanitarian activities that require creativity approaches of reducing their damage. This narrative review focuses on the technology intersection with infectious disease response in conflict zones, and complexity of healthcare infrastructure, population displacement, and security risks. This narrative review explores how conflict-related destruction is harmful towards healthcare systems and the impediments to disease surveillance and response activities. In this regards, the review also considered the contributions of technological innovations, such as the improvement of epidemiological surveillance, mobile health (mHealth) technologies, genomic sequencing, and surveillance technologies, in strengthening infectious disease management in conflict settings. Ethical issues related to data privacy, security and fairness are also covered. By advisement on policy that focuses on investment in surveillance systems, diagnostic capacity, capacity building, collaboration, and even ethical governance, stakeholders can leverage technology to enhance the response to infectious disease in conflict settings and, thus, protect the global health security. This review is full of information for researchers, policymakers, and practitioners who are dealing with the issues of infectious disease outbreaks in conflicts worn areas.
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Affiliation(s)
| | - Esther Ugo Alum
- Department of Publication and Extension, Kampala International University, Uganda, Kampala, Uganda
| | - Jovita Nnenna Ugwu
- Department of Publication and Extension, Kampala International University, Uganda, Kampala, Uganda
| | - Val Hyginus Udoka Eze
- Department of Publication and Extension, Kampala International University, Uganda, Kampala, Uganda
| | - Chinyere N Ugwu
- Department of Publication and Extension, Kampala International University, Uganda, Kampala, Uganda
| | - Fabian C Ogenyi
- Department of Publication and Extension, Kampala International University, Uganda, Kampala, Uganda
| | - Michael Ben Okon
- Department of Publication and Extension, Kampala International University, Uganda, Kampala, Uganda
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Contreras DA, Cencetti G, Barrat A. Infection patterns in simple and complex contagion processes on networks. PLoS Comput Biol 2024; 20:e1012206. [PMID: 38857274 PMCID: PMC11192313 DOI: 10.1371/journal.pcbi.1012206] [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: 09/19/2023] [Revised: 06/21/2024] [Accepted: 05/28/2024] [Indexed: 06/12/2024] Open
Abstract
Contagion processes, representing the spread of infectious diseases, information, or social behaviors, are often schematized as taking place on networks, which encode for instance the interactions between individuals. The impact of the network structure on spreading process has been widely investigated, but not the reverse question: do different processes unfolding on a given network lead to different infection patterns? How do the infection patterns depend on a model's parameters or on the nature of the contagion processes? Here we address this issue by investigating the infection patterns for a variety of models. In simple contagion processes, where contagion events involve one connection at a time, we find that the infection patterns are extremely robust across models and parameters. In complex contagion models instead, in which multiple interactions are needed for a contagion event, non-trivial dependencies on models parameters emerge, as the infection pattern depends on the interplay between pairwise and group contagions. In models involving threshold mechanisms moreover, slight parameter changes can significantly impact the spreading paths. Our results show that it is possible to study crucial features of a spread from schematized models, and inform us on the variations between spreading patterns in processes of different nature.
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Affiliation(s)
- Diego Andrés Contreras
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
| | - Giulia Cencetti
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
- Fondazione Bruno Kessler, Trento, Italy
| | - Alain Barrat
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
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3
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Fu L, Yang Q, Liu X, He L. Risk assessment of infectious disease epidemic based on fuzzy Bayesian network. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024; 44:40-53. [PMID: 37038093 DOI: 10.1111/risa.14137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 02/27/2023] [Accepted: 03/02/2023] [Indexed: 06/19/2023]
Abstract
The prevention and control of infectious disease epidemic (IDE) is an important task for every country and region. Risk assessment is significant for the prevention and control of IDE. Fuzzy Bayesian networks (FBN) can capture complex causality and uncertainty. The study developed a novel FBN model, integrating grounded theory, interpretive structural model, and expert weight determination algorithm for the risk assessment of IDE. The algorithm is proposed by the authors for expert weighting in fuzzy environment. The proposed FBN model comprehensively takes into account the risk factors and the interaction among them, and quantifies the uncertainty of IDE risk assessment, so as to make the assessment results more reliable. Taking the epidemic situation of COVID-19 in Wuhan as a case, the application of the proposed model is illustrated. And sensitivity analysis is performed to identify the important risk factors of IDE. Moreover, the effectiveness of the model is checked by the three-criterion-based quantitative validation method including variation connection, consistent effect, and cumulative limitation. Results show that the probability of the outbreak of COVID-19 in Wuhan is as high as 82.26%, which is well-matched with the actual situation. "Information transfer mechanism," "coordination and cooperation among various personnel," "population flow," and "ability of quarantine" are key risk factors. The constructed model meets the above three criteria. The application potential and effectiveness of the developed FBN model are demonstrated. The study provides decision support for preventing and controlling IDE.
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Affiliation(s)
- Lingmei Fu
- College of Emergency Management, Nanjing Tech University, Nanjing, Jiangsu, China
| | - Qing Yang
- School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei, China
| | - Xingxing Liu
- School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei, China
| | - Ling He
- School of Management, Wuhan Institute of Technology, Wuhan, Hubei, China
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Klamser PP, Zachariae A, Maier BF, Baranov O, Jongen C, Schlosser F, Brockmann D. Inferring country-specific import risk of diseases from the world air transportation network. PLoS Comput Biol 2024; 20:e1011775. [PMID: 38266041 PMCID: PMC10843136 DOI: 10.1371/journal.pcbi.1011775] [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: 05/03/2023] [Revised: 02/05/2024] [Accepted: 12/21/2023] [Indexed: 01/26/2024] Open
Abstract
Disease propagation between countries strongly depends on their effective distance, a measure derived from the world air transportation network (WAN). It reduces the complex spreading patterns of a pandemic to a wave-like propagation from the outbreak country, establishing a linear relationship to the arrival time of the unmitigated spread of a disease. However, in the early stages of an outbreak, what concerns decision-makers in countries is understanding the relative risk of active cases arriving in their country-essentially, the likelihood that an active case boarding an airplane at the outbreak location will reach them. While there are data-fitted models available to estimate these risks, accurate mechanistic, parameter-free models are still lacking. Therefore, we introduce the 'import risk' model in this study, which defines import probabilities using the effective-distance framework. The model assumes that airline passengers are distributed along the shortest path tree that starts at the outbreak's origin. In combination with a random walk, we account for all possible paths, thus inferring predominant connecting flights. Our model outperforms other mobility models, such as the radiation and gravity model with varying distance types, and it improves further if additional geographic information is included. The import risk model's precision increases for countries with stronger connections within the WAN, and it reveals a geographic distance dependence that implies a pull- rather than a push-dynamic in the distribution process.
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Affiliation(s)
- Pascal P. Klamser
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Adrian Zachariae
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Benjamin F. Maier
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
- DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark
- Copenhagen Center for Social Data Science, University of Copenhagen, Copenhagen, Denmark
| | - Olga Baranov
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Clara Jongen
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Frank Schlosser
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
| | - Dirk Brockmann
- Department of Biology, Institute for Theoretical Biology, Humboldt‐Universität zu Berlin, Berlin, Germany
- Robert Koch Institute, Berlin, Germany
- Center Synergy of Systems (SynoSys), Center for Interdisciplinary Digital Sciences, Technische Universität Dresden, Dresden, Germany
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Aronoff-Spencer E, Mazrouee S, Graham R, Handcock MS, Nguyen K, Nebeker C, Malekinejad M, Longhurst CA. Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting. PLoS One 2023; 18:e0287368. [PMID: 37594936 PMCID: PMC10437830 DOI: 10.1371/journal.pone.0287368] [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: 07/15/2022] [Accepted: 05/29/2023] [Indexed: 08/20/2023] Open
Abstract
PURPOSE Digital methods to augment traditional contact tracing approaches were developed and deployed globally during the COVID-19 pandemic. These "Exposure Notification (EN)" systems present new opportunities to support public health interventions. To date, there have been attempts to model the impact of such systems, yet no reports have explored the value of real-time system data for predictive epidemiological modeling. METHODS We investigated the potential to short-term forecast COVID-19 caseloads using data from California's implementation of the Google Apple Exposure Notification (GAEN) platform, branded as CA Notify. CA Notify is a digital public health intervention leveraging resident's smartphones for anonymous EN. We extended a published statistical model that uses prior case counts to investigate the possibility of predicting short-term future case counts and then added EN activity to test for improved forecast performance. Additional predictive value was assessed by comparing the pandemic forecasting models with and without EN activity to the actual reported caseloads from 1-7 days in the future. RESULTS Observation of time series presents noticeable evidence for temporal association of system activity and caseloads. Incorporating earlier ENs in our model improved prediction of the caseload counts. Using Bayesian inference, we found nonzero influence of EN terms with probability one. Furthermore, we found a reduction in both the mean absolute percentage error and the mean squared prediction error, the latter of at least 5% and up to 32% when using ENs over the model without. CONCLUSIONS This preliminary investigation suggests smartphone based ENs can significantly improve the accuracy of short-term forecasting. These predictive models can be readily deployed as local early warning systems to triage resources and interventions.
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Affiliation(s)
- Eliah Aronoff-Spencer
- School of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, United States of America
| | - Sepideh Mazrouee
- School of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, United States of America
| | - Rishi Graham
- School of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, United States of America
| | - Mark S. Handcock
- University of California Los Angeles, Los Angeles, CA, United States of America
| | - Kevin Nguyen
- Herbert Wertheim School of Public Health and Longevity Sciences, University of California San Diego, La Jolla, CA, United States of America
- University of California San Diego Health, San Diego, CA, United States of America
| | - Camille Nebeker
- Herbert Wertheim School of Public Health and Longevity Sciences, University of California San Diego, La Jolla, CA, United States of America
| | - Mohsen Malekinejad
- California Department of Public Health, Sacramento, CA, United States of America
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States of America
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Cheng C, Jiang WM, Fan B, Cheng YC, Hsu YT, Wu HY, Chang HH, Tsou HH. Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models. BMC Public Health 2023; 23:1500. [PMID: 37553650 PMCID: PMC10408098 DOI: 10.1186/s12889-023-16419-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/29/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Mathematical and statistical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. Automatic regressive integrated moving average (ARIMA) models are used for time-series forecasting, but only few models of the 2019 coronavirus disease (COVID-19) pandemic have incorporated protective behaviors or vaccination, known to be effective for pandemic control. METHODS To improve the accuracy of prediction, we applied newly developed ARIMA models with predictors (mask wearing, avoiding going out, and vaccination) to forecast weekly COVID-19 case growth rates in Canada, France, Italy, and Israel between January 2021 and March 2022. The open-source data was sourced from the YouGov survey and Our World in Data. Prediction performance was evaluated using the root mean square error (RMSE) and the corrected Akaike information criterion (AICc). RESULTS A model with mask wearing and vaccination variables performed best for the pandemic period in which the Alpha and Delta viral variants were predominant (before November 2021). A model using only past case growth rates as autoregressive predictors performed best for the Omicron period (after December 2021). The models suggested that protective behaviors and vaccination are associated with the reduction of COVID-19 case growth rates, with booster vaccine coverage playing a particularly vital role during the Omicron period. For example, each unit increase in mask wearing and avoiding going out significantly reduced the case growth rate during the Alpha/Delta period in Canada (-0.81 and -0.54, respectively; both p < 0.05). In the Omicron period, each unit increase in the number of booster doses resulted in a significant reduction of the case growth rate in Canada (-0.03), Israel (-0.12), Italy (-0.02), and France (-0.03); all p < 0.05. CONCLUSIONS The key findings of this study are incorporating behavior and vaccination as predictors led to accurate predictions and highlighted their significant role in controlling the pandemic. These models are easily interpretable and can be embedded in a "real-time" schedule with weekly data updates. They can support timely decision making about policies to control dynamically changing epidemics.
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Affiliation(s)
- Chieh Cheng
- Department of Life Science & Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan
| | - Wei-Ming Jiang
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, 350, Taiwan
| | - Byron Fan
- Brown University, RI, Providence, USA
| | - Yu-Chieh Cheng
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, 350, Taiwan
| | - Ya-Ting Hsu
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, 350, Taiwan
| | - Hsiao-Yu Wu
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, 350, Taiwan
| | - Hsiao-Han Chang
- Department of Life Science & Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan
| | - Hsiao-Hui Tsou
- Institute of Population Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, 350, Taiwan.
- Graduate Institute of Biostatistics, College of Public Health, China Medical University, Taichung, Taiwan.
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Sun HC, Pei S, Wang L, Sun YY, Xu XK. The Impact of Spring Festival Travel on Epidemic Spreading in China. Viruses 2023; 15:1527. [PMID: 37515214 PMCID: PMC10384880 DOI: 10.3390/v15071527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 07/03/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
The large population movement during the Spring Festival travel in China can considerably accelerate the spread of epidemics, especially after the relaxation of strict control measures against COVID-19. This study aims to assess the impact of population migration in Spring Festival holiday on epidemic spread under different scenarios. Using inter-city population movement data, we construct the population flow network during the non-holiday time as well as the Spring Festival holiday. We build a large-scale metapopulation model to simulate the epidemic spread among 371 Chinese cities. We analyze the impact of Spring Festival travel on the peak timing and peak magnitude nationally and in each city. Assuming an R0 (basic reproduction number) of 15 and the initial conditions as the reported COVID-19 infections on 17 December 2022, model simulations indicate that the Spring Festival travel can substantially increase the national peak magnitude of infection. The infection peaks arrive at most cities 1-4 days earlier as compared to those of the non-holiday time. While peak infections in certain large cities, such as Beijing and Shanghai, are decreased due to the massive migration of people to smaller cities during the pre-Spring Festival period, peak infections increase significantly in small- or medium-sized cities. For a less transmissible disease (R0 = 5), infection peaks in large cities are delayed until after the Spring Festival. Small- or medium-sized cities may experience a larger infection due to the large-scale population migration from metropolitan areas. The increased disease burden may impose considerable strain on the healthcare systems in these resource-limited areas. For a less transmissible disease, particular attention needs to be paid to outbreaks in large cities when people resume work after holidays.
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Affiliation(s)
- Hao-Chen Sun
- College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China; (H.-C.S.); (Y.-Y.S.)
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK;
| | - Yuan-Yuan Sun
- College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China; (H.-C.S.); (Y.-Y.S.)
| | - Xiao-Ke Xu
- Computational Communication Research Center, Beijing Normal University, Zhuhai 519087, China
- School of Journalism and Communication, Beijing Normal University, Beijing 100875, China
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Pujante-Otalora L, Canovas-Segura B, Campos M, Juarez JM. The use of networks in spatial and temporal computational models for outbreak spread in epidemiology: A systematic review. J Biomed Inform 2023; 143:104422. [PMID: 37315830 DOI: 10.1016/j.jbi.2023.104422] [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: 11/15/2022] [Revised: 06/05/2023] [Accepted: 06/09/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVES To examine recent literature in order to present a comprehensive overview of the current trends as regards the computational models used to represent the propagation of an infectious outbreak in a population, paying particular attention to those that represent network-based transmission. METHODS a systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Papers published in English between 2010 and September 2021 were sought in the ACM Digital Library, IEEE Xplore, PubMed and Scopus databases. RESULTS Upon considering their titles and abstracts, 832 papers were obtained, of which 192 were selected for a full content-body check. Of these, 112 studies were eventually deemed suitable for quantitative and qualitative analysis. Emphasis was placed on the spatial and temporal scales studied, the use of networks or graphs, and the granularity of the data used to evaluate the models. The models principally used to represent the spreading of outbreaks have been stochastic (55.36%), while the type of networks most frequently used are relationship networks (32.14%). The most common spatial dimension used is a region (19.64%) and the most used unit of time is a day (28.57%). Synthetic data as opposed to an external source were used in 51.79% of the papers. With regard to the granularity of the data sources, aggregated data such as censuses or transportation surveys are the most common. CONCLUSION We identified a growing interest in the use of networks to represent disease transmission. We detected that research is focused on only certain combinations of the computational model, type of network (in both the expressive and the structural sense) and spatial scale, while the search for other interesting combinations has been left for the future.
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Affiliation(s)
- Lorena Pujante-Otalora
- AIKE Research Group (INTICO), University of Murcia, Campus Espinardo, Murcia 30100, Spain.
| | | | - Manuel Campos
- AIKE Research Group (INTICO), University of Murcia, Campus Espinardo, Murcia 30100, Spain; Murcian Bio-Health Institute (IMIB-Arrixaca), El Palmar, Murcia 30120, Spain.
| | - Jose M Juarez
- AIKE Research Group (INTICO), University of Murcia, Campus Espinardo, Murcia 30100, Spain.
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Lee J, Liu H, Abdel-Aty M. Changes in traffic crash patterns: Before and after the outbreak of COVID-19 in Florida. ACCIDENT; ANALYSIS AND PREVENTION 2023; 190:107187. [PMID: 37364361 DOI: 10.1016/j.aap.2023.107187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 05/24/2023] [Accepted: 06/19/2023] [Indexed: 06/28/2023]
Abstract
In the twentieth year of the twenty-first century, humanity is facing an unprecedented global crisis owing to the COVID-19 pandemic. It has brought about drastic changes in the way we live and work, as well as the way we move from one place to another, namely transportation. Previous studies have preliminarily found that mobility, travel behavior, and road traffic safety status experienced great changes after the outbreak of the COVID-19. The objective of this study is to explore how crash patterns have changed, as well as the contributing factors of such changes and the heterogeneity between counties in Florida. Thus, data of COVID-19 cases, crash, socioeconomic factors, and traffic volume of 2019 and 2020 are collected. Preliminary analyses show a considerable reduction from March to June. Substantial changes are shown in the proportions of crashes by time of occurrence and injury severity. Two types of statistical models are developed to identify factors of (1) changes in the percentages of crashes by type and (2) the numbers of crashes by type. The developed models reveal various demographic, socioeconomic, and travel factors. After controlling other factors, the total numbers of crashes are 14% lower after the outbreak. The most significant reductions are observed in peak-hour (22%), while no significant change is found in fatal crashes. The results show that the number of crashes has significantly decreased even after controlling the traffic volume, but some crash types (e.g., fatal) did not show a significant reduction. The findings are expected to provide some insights into better transportation planning and management to ensure traffic safety in a possible future epidemic.
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Affiliation(s)
- Jaeyoung Lee
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China; Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, United States.
| | - Haiyan Liu
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China.
| | - Mohamed Abdel-Aty
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, United States.
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Caldarelli G, Arcaute E, Barthelemy M, Batty M, Gershenson C, Helbing D, Mancuso S, Moreno Y, Ramasco JJ, Rozenblat C, Sánchez A, Fernández-Villacañas JL. The role of complexity for digital twins of cities. NATURE COMPUTATIONAL SCIENCE 2023; 3:374-381. [PMID: 38177836 DOI: 10.1038/s43588-023-00431-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 03/13/2023] [Indexed: 01/06/2024]
Abstract
We argue that theories and methods drawn from complexity science are urgently needed to guide the development and use of digital twins for cities. The theoretical framework from complexity science takes into account both the short-term and the long-term dynamics of cities and their interactions. This is the foundation for a new approach that treats cities not as large machines or logistic systems but as mutually interwoven self-organizing phenomena, which evolve, to an extent, like living systems.
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Affiliation(s)
- G Caldarelli
- DSMN University of Venice Ca'Foscari, Venice, Italy.
- ISC-CNR, Dipartimento di Fisica, Università Sapienza, Rome, Italy.
- London Institute for Mathematical Sciences, London, UK.
- Fondazione per il futuro delle città, Florence, Italy.
| | - E Arcaute
- CASA,The Bartlett Centre for Advanced Spatial Analysis, UCL, London, UK
- The Alan Turing Institute, The British Library, London, UK
| | - M Barthelemy
- Université Paris-Saclay, CNRS, CEA, Institut de Physique Théorique, Gif-sur-Yvette, France
- Centre d'Analyse et de Mathématique Sociales CAMS, UMR 8557 CNRS-EHESS, Ecole des Hautes Etudes en Sciences Sociales, Paris, France
| | - M Batty
- CASA,The Bartlett Centre for Advanced Spatial Analysis, UCL, London, UK
- The Alan Turing Institute, The British Library, London, UK
| | - C Gershenson
- Universidad Nacional Autónoma de México, Mexico City, Mexico
- Santa Fe Institute, Santa Fe, NM, USA
| | - D Helbing
- ETH Zurich, Computational Social Science, Zurich, Switzerland
- Complexity Science Hub, Vienna, Austria
| | - S Mancuso
- Fondazione per il futuro delle città, Florence, Italy
- Department of Agriculture, Food, Environment and Forestry (DAGRI), Florence, Italy
| | - Y Moreno
- Complexity Science Hub, Vienna, Austria
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain
- Department of Theoretical Physics, Faculty of Sciences, University of Zaragoza, Zaragoza, Spain
- CENTAI Institute, Turin, Italy
| | - J J Ramasco
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Palma de Mallorca, Spain
| | - C Rozenblat
- Institute of Geography and Sustainability, UNIL, Lausanne, Switzerland
| | - A Sánchez
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Departamento de Matematicas, Universidad Carlos III de Madrid, Getafe, Spain
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11
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Ma S, Ning S, Yang S. Joint COVID-19 and influenza-like illness forecasts in the United States using internet search information. COMMUNICATIONS MEDICINE 2023; 3:39. [PMID: 36964311 PMCID: PMC10038385 DOI: 10.1038/s43856-023-00272-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 03/09/2023] [Indexed: 03/26/2023] Open
Abstract
BACKGROUND As the prolonged COVID-19 pandemic continues, severe seasonal Influenza (flu) may happen alongside COVID-19. This could cause a "twindemic", in which there are additional burdens on health care resources and public safety compared to those occurring in the presence of a single infection. Amidst the raising trend of co-infections of the two diseases, forecasting both Influenza-like Illness (ILI) outbreaks and COVID-19 waves in a reliable and timely manner becomes more urgent than ever. Accurate and real-time joint prediction of the twindemic aids public health organizations and policymakers in adequate preparation and decision making. However, in the current pandemic, existing ILI and COVID-19 forecasting models face shortcomings under complex inter-disease dynamics, particularly due to the similarities in symptoms and healthcare-seeking patterns of the two diseases. METHODS Inspired by the interconnection between ILI and COVID-19 activities, we combine related internet search and bi-disease time series information for the U.S. national level and state level forecasts. Our proposed ARGOX-Joint-Ensemble adopts a new ensemble framework that integrates ILI and COVID-19 disease forecasting models to pool the information between the two diseases and provide joint multi-resolution and multi-target predictions. Through a winner-takes-all ensemble fashion, our framework is able to adaptively select the most predictive COVID-19 or ILI signals. RESULTS In the retrospective evaluation, our model steadily outperforms alternative benchmark methods, and remains competitive with other publicly available models in both point estimates and probabilistic predictions (including intervals). CONCLUSIONS The success of our approach illustrates that pooling information between the ILI and COVID-19 leads to improved forecasting models than individual models for either of the disease.
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Affiliation(s)
- Simin Ma
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shaoyang Ning
- Department of Mathematics and Statistics, Williams College, Williamstown, MA, 01267, USA
| | - Shihao Yang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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12
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Mazzoli M, Gallotti R, Privitera F, Colet P, Ramasco JJ. Spatial immunization to abate disease spreading in transportation hubs. Nat Commun 2023; 14:1448. [PMID: 36941266 PMCID: PMC10027826 DOI: 10.1038/s41467-023-36985-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 02/27/2023] [Indexed: 03/23/2023] Open
Abstract
Proximity social interactions are crucial for infectious diseases transmission. Crowded agglomerations pose serious risk of triggering superspreading events. Locations like transportation hubs (airports and stations) are designed to optimize logistic efficiency, not to reduce crowding, and are characterized by a constant in and out flow of people. Here, we analyze the paradigmatic example of London Heathrow, one of the busiest European airports. Thanks to a dataset of anonymized individuals' trajectories, we can model the spreading of different diseases to localize the contagion hotspots and to propose a spatial immunization policy targeting them to reduce disease spreading risk. We also detect the most vulnerable destinations to contagions produced at the airport and quantify the benefits of the spatial immunization technique to prevent regional and global disease diffusion. This method is immediately generalizable to train, metro and bus stations and to other facilities such as commercial or convention centers.
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Affiliation(s)
- Mattia Mazzoli
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain.
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, IPLESP, Paris, France.
| | - Riccardo Gallotti
- CHuB Lab, Fondazione Bruno Kessler, Via Sommarive 18, 38123, Povo (TN), Trento, Italy
| | | | - Pere Colet
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain
| | - José J Ramasco
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain.
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13
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de Miguel Arribas A, Aleta A, Moreno Y. Assessing the effectiveness of perimeter lockdowns as a response to epidemics at the urban scale. Sci Rep 2023; 13:4474. [PMID: 36934138 PMCID: PMC10024032 DOI: 10.1038/s41598-023-31614-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/14/2023] [Indexed: 03/20/2023] Open
Abstract
From September 2020 to May 2021 Madrid region (Spain) followed a rather unique non-pharmaceutical intervention (NPI) by establishing a strategy of perimeter lockdowns (PLs) that banned travels to and from areas satisfying certain epidemiological risk criteria. PLs were pursued to avoid harsher restrictions, but some studies have found that the particular implementation by Madrid authorities was rather ineffective. Based on Madrid's case, we devise a general, minimal framework to investigate the PLs effectiveness by using a data-driven metapopulation epidemiological model of a city, and explore under which circumstances the PLs could be a good NPI. The model is informed with real mobility data from Madrid to contextualize its results, but it can be generalized elsewhere. The lowest lockdown activation threshold [Formula: see text] considered (14-day cumulative incidence rate of 20 cases per every [Formula: see text] inhabitants) shows a prevalence reduction [Formula: see text] with respect to the scenario [Formula: see text], more akin to the case of Madrid, and assuming no further mitigation. Only the combination of [Formula: see text] and mobility reduction [Formula: see text] can avoid PLs for more than [Formula: see text] of the system. The combination of low [Formula: see text] and strong local transmissibility reduction is key to minimize the impact, but the latter is harder to achieve given that we assume a situation with highly mitigated transmission, resembling the one observed during the second wave of COVID-19 in Madrid. Thus, we conclude that a generalized lockdown is hard to avoid under any realistic setting if only this strategy is applied.
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Affiliation(s)
- Alfonso de Miguel Arribas
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain.
- Department of Theoretical Physics, University of Zaragoza, 50018, Zaragoza, Spain.
| | - Alberto Aleta
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain
- Department of Theoretical Physics, University of Zaragoza, 50018, Zaragoza, Spain
| | - Yamir Moreno
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018, Zaragoza, Spain
- Department of Theoretical Physics, University of Zaragoza, 50018, Zaragoza, Spain
- Centai, Turin, Italy
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14
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Foini P, Tizzoni M, Martini G, Paolotti D, Omodei E. On the forecastability of food insecurity. Sci Rep 2023; 13:2793. [PMID: 36928341 PMCID: PMC10038988 DOI: 10.1038/s41598-023-29700-y] [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: 09/16/2022] [Accepted: 02/09/2023] [Indexed: 03/18/2023] Open
Abstract
Food insecurity, defined as the lack of physical or economic access to safe, nutritious and sufficient food, remains one of the main challenges included in the 2030 Agenda for Sustainable Development. Near real-time data on the food insecurity situation collected by international organizations such as the World Food Programme can be crucial to monitor and forecast time trends of insufficient food consumption levels in countries at risk. Here, using food consumption observations in combination with secondary data on conflict, extreme weather events and economic shocks, we build a forecasting model based on gradient boosted regression trees to create predictions on the evolution of insufficient food consumption trends up to 30 days in to the future in 6 countries (Burkina Faso, Cameroon, Mali, Nigeria, Syria and Yemen). Results show that the number of available historical observations is a key element for the forecasting model performance. Among the 6 countries studied in this work, for those with the longest food insecurity time series, that is Syria and Yemen, the proposed forecasting model allows to forecast the prevalence of people with insufficient food consumption up to 30 days into the future with higher accuracy than a naive approach based on the last measured prevalence only. The framework developed in this work could provide decision makers with a tool to assess how the food insecurity situation will evolve in the near future in countries at risk. Results clearly point to the added value of continuous near real-time data collection at sub-national level.
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Affiliation(s)
- Pietro Foini
- ISI Foundation, Via Chisola 5, 10126, Turin, Italy
| | - Michele Tizzoni
- ISI Foundation, Via Chisola 5, 10126, Turin, Italy
- Department of Sociology and Social Research, University of Trento, Via Verdi, 26, 38122, Trento, Italy
| | - Giulia Martini
- World Food Programme, Research, Assessment and Monitoring Division (RAM), Via Cesare Giulio Viola 68, 00148, Rome, Italy
| | | | - Elisa Omodei
- World Food Programme, Research, Assessment and Monitoring Division (RAM), Via Cesare Giulio Viola 68, 00148, Rome, Italy.
- Department of Network and Data Science, Central European University, Quellenstraße 51, 1100, Vienna, Austria.
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15
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Chang SA, Kuan CH, Hung CY, Wang TCC, Chen YS. The outbreak of COVID-19 in Taiwan in late spring 2021: combinations of specific weather conditions and related factors. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:85669-85675. [PMID: 34669130 PMCID: PMC8526532 DOI: 10.1007/s11356-021-17055-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/11/2021] [Indexed: 06/13/2023]
Abstract
This study aimed to investigate the impact of weather conditions on the daily incidence of the COVID-19 pandemic in late spring 2021 in Taiwan, which is unlike the weather conditions of the COVID-19 outbreak in 2020. Meteorological parameters such as maximum daily temperature, relative humidity, and wind speed were included. The Spearman rank correlation test was used to evaluate the relationship between weather and daily domestic COVID-19 cases. The maximum daily temperature had a positively significant correlation with daily new COVID-19 cases within a 14-day lag period, while the relative humidity and wind speed has a fairly high correlation with the number of daily cases within a 13- and 14-day lag, respectively. In addition, the weather characteristics during this period were an increasingly high temperature, with steady high relative humidity and slightly decreasing wind speed. Our study revealed the weather conditions at the time of the domestic outbreak of COVID-19 in Taiwan in May 2021 and the possible association between weather factors and the COVID-19 pandemic. Further large-scale analysis of weather factors is essential for understanding the impact of weather on the spread of infectious diseases.
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Affiliation(s)
- Shih-An Chang
- Department of Chinese Acupuncture and Traumatology, Center of Traditional Chinese Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Chia-Hsuan Kuan
- Department of Chinese Acupuncture and Traumatology, Center of Traditional Chinese Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Chi-Yen Hung
- Department of Chinese Acupuncture and Traumatology, Center of Traditional Chinese Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Tai-Chi Chen Wang
- Department of Atmospheric Sciences, National Central University, Taoyuan, Taiwan
| | - Yu-Sheng Chen
- Department of Chinese Acupuncture and Traumatology, Center of Traditional Chinese Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Graduate Institute of Clinical Medical Sciences, Chang Gung University, Taoyuan, Taiwan
- Taiwan Huangdi‑Neijing Medical Practice Association (THMPA), Taoyuan, Taiwan
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16
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The Impact of Urbanization and Human Mobility on Seasonal Influenza in Northern China. Viruses 2022; 14:v14112563. [PMID: 36423173 PMCID: PMC9697484 DOI: 10.3390/v14112563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/14/2022] [Accepted: 11/17/2022] [Indexed: 11/22/2022] Open
Abstract
The intensity of influenza epidemics varies significantly from year to year among regions with similar climatic conditions and populations. However, the underlying mechanisms of the temporal and spatial variations remain unclear. We investigated the impact of urbanization and public transportation size on influenza activity. We used 6-year weekly provincial-level surveillance data of influenza-like disease incidence (ILI) and viral activity in northern China. We derived the transmission potential of influenza for each epidemic season using the susceptible-exposed-infectious-removed-susceptible (SEIRS) model and estimated the transmissibility in the peak period via the instantaneous reproduction number (Rt). Public transport was found to explain approximately 28% of the variance in the seasonal transmission potential. Urbanization and public transportation size explained approximately 10% and 21% of the variance in maximum Rt in the peak period, respectively. For the mean Rt during the peak period, urbanization and public transportation accounted for 9% and 16% of the variance in Rt, respectively. Our results indicated that the differences in the intensity of influenza epidemics among the northern provinces of China were partially driven by urbanization and public transport size. These findings are beneficial for predicting influenza intensity and developing preparedness strategies for the early stages of epidemics.
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17
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Tan Y, Zhang Y, Cheng X, Zhou XH. Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions. Sci Rep 2022; 12:16630. [PMID: 36198691 PMCID: PMC9534028 DOI: 10.1038/s41598-022-18775-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 08/18/2022] [Indexed: 11/09/2022] Open
Abstract
A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models.
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Affiliation(s)
- Yixuan Tan
- Department of Mathematics, Duke University, Durham, USA
| | - Yuan Zhang
- School of Statistics, Renmin University of China, Beijing, China
| | - Xiuyuan Cheng
- Department of Mathematics, Duke University, Durham, USA.
| | - Xiao-Hua Zhou
- Center for Statistical Sciences, Peking University, Beijing, China.
- Beijing International Center for Mathematical Research, Peking University, Beijing, China.
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China.
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18
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Le Treut G, Huber G, Kamb M, Kawagoe K, McGeever A, Miller J, Pnini R, Veytsman B, Yllanes D. A high-resolution flux-matrix model describes the spread of diseases in a spatial network and the effect of mitigation strategies. Sci Rep 2022; 12:15946. [PMID: 36153391 PMCID: PMC9509367 DOI: 10.1038/s41598-022-19931-w] [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: 01/23/2022] [Accepted: 09/06/2022] [Indexed: 12/01/2022] Open
Abstract
Propagation of an epidemic across a spatial network of communities is described by a variant of the SIR model accompanied by an intercommunity infectivity matrix. This matrix is estimated from fluxes between communities, obtained from cell-phone tracking data recorded in the USA between March 2020 and February 2021. We apply this model to the SARS-CoV-2 pandemic by fitting just one global parameter representing the frequency of interaction between individuals. We find that the predicted infections agree reasonably well with the reported cases. We clearly see the effect of “shelter-in-place” policies introduced at the onset of the pandemic. Interestingly, a model with uniform transmission rates produces similar results, suggesting that the epidemic transmission was deeply influenced by air travel. We then study the effect of alternative mitigation policies, in particular restricting long-range travel. We find that this policy is successful in decreasing the epidemic size and slowing down the spread, but less effective than the shelter-in-place policy. This policy can result in a pulled wave of infections. We express its velocity and characterize the shape of the traveling front as a function of the epidemiological parameters. Finally, we discuss a policy of selectively constraining travel based on an edge-betweenness criterion.
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19
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Tsori Y, Granek R. Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina. PLoS One 2022; 17:e0268995. [PMID: 35679238 PMCID: PMC9182687 DOI: 10.1371/journal.pone.0268995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 05/13/2022] [Indexed: 11/18/2022] Open
Abstract
During the COVID-19 pandemic authorities have been striving to obtain reliable predictions for the spreading dynamics of the disease. We recently developed a multi-“sub-populations” (multi-compartments: susceptible, exposed, pre-symptomatic, infectious, recovered) model, that accounts for the spatial in-homogeneous spreading of the infection and shown, for a variety of examples, how the epidemic curves are highly sensitive to location of epicenters, non-uniform population density, and local restrictions. In the present work we test our model against real-life data from South Carolina during the period May 22 to July 22 (2020). During this period, minimal restrictions have been employed, which allowed us to assume that the local basic reproduction number is constant in time. We account for the non-uniform population density in South Carolina using data from NASA’s Socioeconomic Data and Applications Center (SEDAC), and predict the evolution of infection heat-maps during the studied period. Comparing the predicted heat-maps with those observed, we find high qualitative resemblance. Moreover, the Pearson’s correlation coefficient is relatively high thus validating our model against real-world data. We conclude that the model accounts for the major effects controlling spatial in-homogeneous spreading of the disease. Inclusion of additional sub-populations (compartments), in the spirit of several recently developed models for COVID-19, can be easily performed within our mathematical framework.
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Affiliation(s)
- Yoav Tsori
- Department of Chemical Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
- The Ilse Katz Institute for Nanoscale Science and Technology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- * E-mail:
| | - Rony Granek
- The Avram and Stella Goldstein-Gorren Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
- The Ilse Katz Institute for Nanoscale Science and Technology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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20
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Zerenner T, Di Lauro F, Dashti M, Berthouze L, Kiss IZ. Probabilistic predictions of SIS epidemics on networks based on population-level observations. Math Biosci 2022; 350:108854. [PMID: 35659615 DOI: 10.1016/j.mbs.2022.108854] [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: 11/03/2021] [Revised: 05/17/2022] [Accepted: 05/23/2022] [Indexed: 11/16/2022]
Abstract
We predict the future course of ongoing susceptible-infected-susceptible (SIS) epidemics on regular, Erdős-Rényi and Barabási-Albert networks. It is known that the contact network influences the spread of an epidemic within a population. Therefore, observations of an epidemic, in this case at the population-level, contain information about the underlying network. This information, in turn, is useful for predicting the future course of an ongoing epidemic. To exploit this in a prediction framework, the exact high-dimensional stochastic model of an SIS epidemic on a network is approximated by a lower-dimensional surrogate model. The surrogate model is based on a birth-and-death process; the effect of the underlying network is described by a parametric model for the birth rates. We demonstrate empirically that the surrogate model captures the intrinsic stochasticity of the epidemic once it reaches a point from which it will not die out. Bayesian parameter inference allows for uncertainty about the model parameters and the class of the underlying network to be incorporated directly into probabilistic predictions. An evaluation of a number of scenarios shows that in most cases the resulting prediction intervals adequately quantify the prediction uncertainty. As long as the population-level data is available over a long-enough period, even if not sampled frequently, the model leads to excellent predictions where the underlying network is correctly identified and prediction uncertainty mainly reflects the intrinsic stochasticity of the spreading epidemic. For predictions inferred from shorter observational periods, uncertainty about parameters and network class dominate prediction uncertainty. The proposed method relies on minimal data at population-level, which is always likely to be available. This, combined with its numerical efficiency, makes the proposed method attractive to be used either as a standalone inference and prediction scheme or in conjunction with other inference and/or predictive models.
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Affiliation(s)
- T Zerenner
- Department of Mathematics, University of Sussex, Falmer, Brighton, BN1 9QH, UK.
| | - F Di Lauro
- Department of Mathematics, University of Sussex, Falmer, Brighton, BN1 9QH, UK; Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7FL, UK
| | - M Dashti
- Department of Mathematics, University of Sussex, Falmer, Brighton, BN1 9QH, UK
| | - L Berthouze
- Department of Informatics, University of Sussex, Falmer, Brighton, BN1 9QH, UK
| | - I Z Kiss
- Department of Mathematics, University of Sussex, Falmer, Brighton, BN1 9QH, UK.
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21
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Contreras DA, Colosi E, Bassignana G, Colizza V, Barrat A. Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases. J R Soc Interface 2022; 19:20220164. [PMID: 35730172 PMCID: PMC9214285 DOI: 10.1098/rsif.2022.0164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/31/2022] [Indexed: 11/12/2022] Open
Abstract
Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation.
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Affiliation(s)
- Diego Andrés Contreras
- Aix Marseille University, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
| | - Elisabetta Colosi
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Giulia Bassignana
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
- Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
| | - Alain Barrat
- Aix Marseille University, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
- Tokyo Tech World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan
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22
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Contrasting social and non-social sources of predictability in human mobility. Nat Commun 2022; 13:1922. [PMID: 35395828 PMCID: PMC8993859 DOI: 10.1038/s41467-022-29592-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 03/22/2022] [Indexed: 11/15/2022] Open
Abstract
Social structures influence human behavior, including their movement patterns. Indeed, latent information about an individual’s movement can be present in the mobility patterns of both acquaintances and strangers. We develop a “colocation” network to distinguish the mobility patterns of an ego’s social ties from those not socially connected to the ego but who arrive at a location at a similar time as the ego. Using entropic measures, we analyze and bound the predictive information of an individual’s mobility pattern and its flow to both types of ties. While the former generically provide more information, replacing up to 94% of an ego’s predictability, significant information is also present in the aggregation of unknown colocators, that contain up to 85% of an ego’s predictive information. Such information flow raises privacy concerns: individuals sharing data via mobile applications may be providing actionable information on themselves as well as others whose data are absent. Information about an individual’s mobility can leave traces embedded in the social network. The authors show that such traces are also present beyond the social network. Simple colocation contains predictive information about one’s mobility patterns even when the colocators have no social links. In the aggregate, non-social information can sometimes meet or exceed social information.
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23
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Valgañón P, Soriano-Paños D, Arenas A, Gómez-Gardeñes J. Contagion-diffusion processes with recurrent mobility patterns of distinguishable agents. CHAOS (WOODBURY, N.Y.) 2022; 32:043102. [PMID: 35489866 DOI: 10.1063/5.0085532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 03/16/2022] [Indexed: 06/14/2023]
Abstract
The analysis of contagion-diffusion processes in metapopulations is a powerful theoretical tool to study how mobility influences the spread of communicable diseases. Nevertheless, many metapopulation approaches use indistinguishable agents to alleviate analytical difficulties. Here, we address the impact that recurrent mobility patterns, and the spatial distribution of distinguishable agents, have on the unfolding of epidemics in large urban areas. We incorporate the distinguishable nature of agents regarding both their residence and their usual destination. The proposed model allows both a fast computation of the spatiotemporal pattern of the epidemic trajectory and the analytical calculation of the epidemic threshold. This threshold is found as the spectral radius of a mixing matrix encapsulating the residential distribution and the specific commuting patterns of agents. We prove that the simplification of indistinguishable individuals overestimates the value of the epidemic threshold.
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Affiliation(s)
- P Valgañón
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
| | - D Soriano-Paños
- Instituto Gulbenkian de Ciência (IGC), 2780-156 Oeiras, Portugal
| | - A Arenas
- Departament de Matemáticas i Enginyeria Informática, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - J Gómez-Gardeñes
- Department of Condensed Matter Physics, University of Zaragoza, 50009 Zaragoza, Spain
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24
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Rosenkrantz DJ, Vullikanti A, Ravi SS, Stearns RE, Levin S, Poor HV, Marathe MV. Fundamental limitations on efficiently forecasting certain epidemic measures in network models. Proc Natl Acad Sci U S A 2022; 119:e2109228119. [PMID: 35046025 PMCID: PMC8794801 DOI: 10.1073/pnas.2109228119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 11/05/2021] [Indexed: 11/18/2022] Open
Abstract
The ongoing COVID-19 pandemic underscores the importance of developing reliable forecasts that would allow decision makers to devise appropriate response strategies. Despite much recent research on the topic, epidemic forecasting remains poorly understood. Researchers have attributed the difficulty of forecasting contagion dynamics to a multitude of factors, including complex behavioral responses, uncertainty in data, the stochastic nature of the underlying process, and the high sensitivity of the disease parameters to changes in the environment. We offer a rigorous explanation of the difficulty of short-term forecasting on networked populations using ideas from computational complexity. Specifically, we show that several forecasting problems (e.g., the probability that at least a given number of people will get infected at a given time and the probability that the number of infections will reach a peak at a given time) are computationally intractable. For instance, efficient solvability of such problems would imply that the number of satisfying assignments of an arbitrary Boolean formula in conjunctive normal form can be computed efficiently, violating a widely believed hypothesis in computational complexity. This intractability result holds even under the ideal situation, where all the disease parameters are known and are assumed to be insensitive to changes in the environment. From a computational complexity viewpoint, our results, which show that contagion dynamics become unpredictable for both macroscopic and individual properties, bring out some fundamental difficulties of predicting disease parameters. On the positive side, we develop efficient algorithms or approximation algorithms for restricted versions of forecasting problems.
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Affiliation(s)
- Daniel J Rosenkrantz
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904
- Department of Computer Science, University at Albany-State University of New York, Albany, NY 12222
| | - Anil Vullikanti
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904
- Department of Computer Science, University of Virginia, Charlottesville, VA 22904
| | - S S Ravi
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904
- Department of Computer Science, University at Albany-State University of New York, Albany, NY 12222
| | - Richard E Stearns
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904
- Department of Computer Science, University at Albany-State University of New York, Albany, NY 12222
| | - Simon Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
- Princeton Environmental Institute, Princeton University, Princeton, NJ 08544
| | - H Vincent Poor
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544
| | - Madhav V Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA 22904;
- Department of Computer Science, University of Virginia, Charlottesville, VA 22904
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Tomy A, Razzanelli M, Di Lauro F, Rus D, Della Santina C. Estimating the state of epidemics spreading with graph neural networks. NONLINEAR DYNAMICS 2022; 109:249-263. [PMID: 35079201 PMCID: PMC8777184 DOI: 10.1007/s11071-021-07160-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 10/31/2021] [Indexed: 06/14/2023]
Abstract
When an epidemic spreads into a population, it is often impractical or impossible to continuously monitor all subjects involved. As an alternative, we propose using algorithmic solutions that can infer the state of the whole population from a limited number of measures. We analyze the capability of deep neural networks to solve this challenging task. We base our proposed architecture on Graph Convolutional Neural Networks. As such, it can reason on the effect of the underlying social network structure, which is recognized as the main component in spreading an epidemic. The proposed architecture can reconstruct the entire state with accuracy above 70%, as proven by two scenarios modeled on the CoVid-19 pandemic. The first is a generic homogeneous population, and the second is a toy model of the Boston metropolitan area. Note that no retraining of the architecture is necessary when changing the model.
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Affiliation(s)
- Abhishek Tomy
- Centre of Innovation in Telecommunications and Integration of services, Inria Grenoble - Rhône-Alpes, Inovallée, France
| | | | | | - Daniela Rus
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA United States
| | - Cosimo Della Santina
- Cognitive Robotics Department, Faculty of Mechanical, Maritime and Materials Engineering, TU Delft, Delft, Netherlands
- Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
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Bota A, Holmberg M, Gardner L, Rosvall M. Socioeconomic and environmental patterns behind H1N1 spreading in Sweden. Sci Rep 2021; 11:22512. [PMID: 34795338 PMCID: PMC8602374 DOI: 10.1038/s41598-021-01857-4] [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: 04/16/2021] [Accepted: 11/03/2021] [Indexed: 11/25/2022] Open
Abstract
Identifying the critical factors related to influenza spreading is crucial in predicting and mitigating epidemics. Specifically, uncovering the relationship between epidemic onset and various risk indicators such as socioeconomic, mobility and climate factors can reveal locations and travel patterns that play critical roles in furthering an outbreak. We study the 2009 A(H1N1) influenza outbreaks in Sweden’s municipalities between 2009 and 2015 and use the Generalized Inverse Infection Method (GIIM) to assess the most significant contributing risk factors. GIIM represents an epidemic spreading process on a network: nodes correspond to geographical objects, links indicate travel routes, and transmission probabilities assigned to the links guide the infection process. Our results reinforce existing observations that the influenza outbreaks considered in this study were driven by the country’s largest population centers, while meteorological factors also contributed significantly. Travel and other socioeconomic indicators have a negligible effect. We also demonstrate that by training our model on the 2009 outbreak, we can predict the epidemic onsets in the following five seasons with high accuracy.
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Affiliation(s)
- András Bota
- Integrated Science Lab, Department of Physics, Umeå University, 90187, Umeå, Sweden. .,Embedded Intelligent Systems Lab, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187, Luleå, Sweden.
| | - Martin Holmberg
- Integrated Science Lab, Department of Physics, Umeå University, 90187, Umeå, Sweden
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Martin Rosvall
- Integrated Science Lab, Department of Physics, Umeå University, 90187, Umeå, Sweden
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Determining travel fluxes in epidemic areas. PLoS Comput Biol 2021; 17:e1009473. [PMID: 34705832 PMCID: PMC8550429 DOI: 10.1371/journal.pcbi.1009473] [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: 12/26/2020] [Accepted: 09/23/2021] [Indexed: 01/08/2023] Open
Abstract
Infectious diseases attack humans from time to time and threaten the lives and survival of people all around the world. An important strategy to prevent the spatial spread of infectious diseases is to restrict population travel. With the reduction of the epidemic situation, when and where travel restrictions can be lifted, and how to organize orderly movement patterns become critical and fall within the scope of this study. We define a novel diffusion distance derived from the estimated mobility network, based on which we provide a general model to describe the spatiotemporal spread of infectious diseases with a random diffusion process and a deterministic drift process of the population. We consequently develop a multi-source data fusion method to determine the population flow in epidemic areas. In this method, we first select available subregions in epidemic areas, and then provide solutions to initiate new travel flux among these subregions. To verify our model and method, we analyze the multi-source data from mainland China and obtain a new travel flux triggering scheme in the selected 29 cities with the most active population movements in mainland China. The testable predictions in these selected cities show that reopening the borders in accordance with our proposed travel flux will not cause a second outbreak of COVID-19 in these cities. The finding provides a methodology of re-triggering travel flux during the weakening spread stage of the epidemic. Human infectious diseases spread from their origins to other places with population movements. In order to curb the spatial spread of infectious diseases, many countries and regions may introduce some travel restrictions when the epidemic is severe, and reopen the borders as the epidemic eases. This process involves some important issues such as the start and end time of travel restrictions, the geographical scope of the implementation of the exit strategy, and the allowable passenger flow on traffic lines. Here, we integrate multi-source data with a mathematical model, and consequently develop a new method to determine the travel flux in epidemic areas. As an application, we use this method to calculate when and where the travel restrictions targeting COVID-19 in China in early 2020 could be lifted, and how to optimize passenger flow along the traffic lines among the reopened cities. The testable predictions indicate that the population flow in accordance with our proposed movement pattern will not cause a resurgent outbreak of COVID-19 in the cities studied.
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Arutkin M, Faranda D, Alberti T, Vallée A. Delayed epidemic peak caused by infection and recovery rate fluctuations. CHAOS (WOODBURY, N.Y.) 2021; 31:101107. [PMID: 34717319 DOI: 10.1063/5.0067625] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 09/13/2021] [Indexed: 06/13/2023]
Abstract
Forecasting epidemic scenarios has been critical to many decision-makers in imposing various public health interventions. Despite progresses in determining the magnitude and timing of epidemics, epidemic peak time predictions for H1N1 and COVID-19 were inaccurate, with the peaks delayed with respect to predictions. Here, we show that infection and recovery rate fluctuations play a critical role in peak timing. Using a susceptible-infected-recovered model with daily fluctuations on control parameters, we show that infection counts follow a lognormal distribution at the beginning of an epidemic wave, similar to price distributions for financial assets. The epidemic peak time of the stochastic solution exhibits an inverse Gaussian probability distribution, fitting the spread of the epidemic peak times observed across Italian regions. We also show that, for a given basic reproduction number R0, the deterministic model anticipates the peak with respect to the most probable and average peak time of the stochastic model. The epidemic peak time distribution allows one for a robust estimation of the epidemic evolution. Considering these results, we believe that the parameters' dynamical fluctuations are paramount to accurately predict the epidemic peak time and should be introduced in epidemiological models.
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Affiliation(s)
- Maxence Arutkin
- UMR CNRS 7083 Gulliver, ESPCI Paris, 10 rue Vauquelin, 75005 Paris, France
| | - Davide Faranda
- Laboratoire des Sciences du Climat et de l'Environnement, CEA Saclay l'Orme des Merisiers, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay & IPSL, 91191 Gif-sur-Yvette, France
| | - Tommaso Alberti
- INAF-Istituto di Astrofisica e Planetologia Spaziali, via del Fosso del Cavaliere 100, 00133 Rome, Italy
| | - Alexandre Vallée
- Department of Clinical Research and Innovation, Foch hospital, 92150 Suresnes, France
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Mazzoli M, Pepe E, Mateo D, Cattuto C, Gauvin L, Bajardi P, Tizzoni M, Hernando A, Meloni S, Ramasco JJ. Interplay between mobility, multi-seeding and lockdowns shapes COVID-19 local impact. PLoS Comput Biol 2021; 17:e1009326. [PMID: 34648495 PMCID: PMC8516261 DOI: 10.1371/journal.pcbi.1009326] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 08/06/2021] [Indexed: 11/22/2022] Open
Abstract
Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. While many factors affect disease incidence at a local level, making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can lead to independent outbreaks that spark from distinct areas of the local contact (social) network. Such mechanism has the potential to boost incidence, making control efforts and contact tracing less effective. Here, through a modeling approach we show that the effect produced by the number of initial infections is non-linear on the incidence peak and peak time. When case importations are carried by mobility from an already infected area, this effect is further enhanced by the local demography and underlying mixing patterns: the impact of every seed is larger in smaller populations. Finally, both in the model simulations and the analysis, we show that a multi-seeding effect combined with mobility restrictions can explain the observed spatial heterogeneities in the first wave of COVID-19 incidence and mortality in five European countries. Our results allow us for identifying what we have called epidemic epicenter: an area that shapes incidence and mortality peaks in the entire country. The present work further clarifies the nonlinear effects that mobility can have on the evolution of an epidemic and highlight their relevance for epidemic control.
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Affiliation(s)
- Mattia Mazzoli
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Palma de Mallorca, Spain
- INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique, IPLESP, Paris, France
| | | | | | | | | | | | | | | | | | - José J. Ramasco
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Palma de Mallorca, Spain
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30
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Barbieri D, Giuliani E, Del Prete A, Losi A, Villani M, Barbieri A. How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18147648. [PMID: 34300099 PMCID: PMC8303245 DOI: 10.3390/ijerph18147648] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 07/07/2021] [Accepted: 07/16/2021] [Indexed: 12/19/2022]
Abstract
The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, and adopt innovative solutions to control it, as is often the case when people are driven by necessity. After 18 months since the first case, it is time to think about the pros and cons of such technologies, including artificial intelligence—which is probably the most complex and misunderstood by non-specialists—in order to get the most out of them, and to suggest future improvements and proper adoption. The aim of this narrative review was to select the relevant papers that directly address the adoption of artificial intelligence and new technologies in the management of pandemics and communicable diseases such as SARS-CoV-2: environmental measures; acquisition and sharing of knowledge in the general population and among clinicians; development and management of drugs and vaccines; remote psychological support of patients; remote monitoring, diagnosis, and follow-up; and maximization and rationalization of human and material resources in the hospital environment.
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Affiliation(s)
- Davide Barbieri
- Department of Neuroscience and Rehabilitation, University of Ferrara, Via Savonarola 9, 44121 Ferrara, Italy;
| | - Enrico Giuliani
- Department of Biomedical, Metabolic and Neuroscience Sciences, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy;
| | - Anna Del Prete
- School of Anesthesiology and Intensive Care, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy; (A.D.P.); (A.B.)
| | - Amanda Losi
- Department of Biomedical, Metabolic and Neuroscience Sciences, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy;
- Correspondence: ; Tel.: +39-0598721234 (ext. 41125)
| | - Matteo Villani
- Department of Anesthesiology and Intensive Care, Azienda USL Piacenza, Via Antonio Anguissola 15, 29121 Piacenza, Italy;
| | - Alberto Barbieri
- School of Anesthesiology and Intensive Care, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy; (A.D.P.); (A.B.)
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Madewell ZJ, Pastore Y Piontti A, Zhang Q, Burton N, Yang Y, Longini IM, Halloran ME, Vespignani A, Dean NE. Using simulated infectious disease outbreaks to inform site selection and sample size for individually randomized vaccine trials during an ongoing epidemic. Clin Trials 2021; 18:630-638. [PMID: 34218667 PMCID: PMC8478719 DOI: 10.1177/17407745211028898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Novel strategies are needed to make vaccine efficacy trials more robust given uncertain epidemiology of infectious disease outbreaks, such as arboviruses like Zika. Spatially resolved mathematical and statistical models can help investigators identify sites at highest risk of future transmission and prioritize these for inclusion in trials. Models can also characterize uncertainty in whether transmission will occur at a site, and how nearby or connected sites may have correlated outcomes. A structure is needed for how trials can use models to address key design questions, including how to prioritize sites, the optimal number of sites, and how to allocate participants across sites. Methods: We illustrate the added value of models using the motivating example of Zika vaccine trial planning during the 2015–2017 Zika epidemic. We used a stochastic, spatially resolved, transmission model (the Global Epidemic and Mobility model) to simulate epidemics and site-level incidence at 100 high-risk sites in the Americas. We considered several strategies for prioritizing sites (average site-level incidence of infection across epidemics, median incidence, probability of exceeding 1% incidence), selecting the number of sites, and allocating sample size across sites (equal enrollment, proportional to average incidence, proportional to rank). To evaluate each design, we stochastically simulated trials in each hypothetical epidemic by drawing observed cases from site-level incidence data. Results: When constraining overall trial size, the optimal number of sites represents a balance between prioritizing highest-risk sites and having enough sites to reduce the chance of observing too few endpoints. The optimal number of sites remained roughly constant regardless of the targeted number of events, although it is necessary to increase the sample size to achieve the desired power. Though different ranking strategies returned different site orders, they performed similarly with respect to trial power. Instead of enrolling participants equally from each site, investigators can allocate participants proportional to projected incidence, though this did not provide an advantage in our example because the top sites had similar risk profiles. Sites from the same geographic region may have similar outcomes, so optimal combinations of sites may be geographically dispersed, even when these are not the highest ranked sites. Conclusion: Mathematical and statistical models may assist in designing successful vaccination trials by capturing uncertainty and correlation in future transmission. Although many factors affect site selection, such as logistical feasibility, models can help investigators optimize site selection and the number and size of participating sites. Although our study focused on trial design for an emerging arbovirus, a similar approach can be made for any infectious disease with the appropriate model for the particular disease.
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Affiliation(s)
- Zachary J Madewell
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Ana Pastore Y Piontti
- Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA, USA
| | - Qian Zhang
- Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA, USA
| | - Nathan Burton
- Institute for Child Health Policy, University of Florida College of Medicine, Gainesville, FL, USA
| | - Yang Yang
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Ira M Longini
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - M Elizabeth Halloran
- Biostatistics, Bioinformatics, and Epidemiology Program, Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.,Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA, USA
| | - Natalie E Dean
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
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Alamo T, G Reina D, Millán Gata P, Preciado VM, Giordano G. Data-driven methods for present and future pandemics: Monitoring, modelling and managing. ANNUAL REVIEWS IN CONTROL 2021; 52:448-464. [PMID: 34220287 PMCID: PMC8238691 DOI: 10.1016/j.arcontrol.2021.05.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 05/29/2023]
Abstract
This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.
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Affiliation(s)
- Teodoro Alamo
- Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Escuela Superior de Ingenieros, Sevilla, Spain
| | - Daniel G Reina
- Departamento de Ingeniería Electrónica, Universidad de Sevilla, Escuela Superior de Ingenieros, Sevilla, Spain
| | - Pablo Millán Gata
- Departamento de Ingeniería, Universidad Loyola Andalucía, Seville, Spain
| | - Victor M Preciado
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA
| | - Giulia Giordano
- Department of Industrial Engineering, University of Trento, Trento, Italy
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Lynch CJ, Gore R. Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study. J Med Internet Res 2021; 23:e24925. [PMID: 33621186 PMCID: PMC7990039 DOI: 10.2196/24925] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 12/07/2020] [Accepted: 02/22/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Forecasting methods rely on trends and averages of prior observations to forecast COVID-19 case counts. COVID-19 forecasts have received much media attention, and numerous platforms have been created to inform the public. However, forecasting effectiveness varies by geographic scope and is affected by changing assumptions in behaviors and preventative measures in response to the pandemic. Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels. OBJECTIVE COVID-19 forecasts keep the public informed and contribute to public policy. As such, proper understanding of forecasting purposes and outcomes is needed to advance knowledge of health statistics for policy makers and the public. Using publicly available real-time data provided online, we aimed to evaluate the performance of seven forecasting methods utilized to forecast cumulative COVID-19 case counts. Forecasts were evaluated based on how well they forecast 1, 3, and 7 days forward when utilizing 1-, 3-, 7-, or all prior-day cumulative case counts during early virus onset. This study provides an objective evaluation of the forecasting methods to identify forecasting model assumptions that contribute to lower error in forecasting COVID-19 cumulative case growth. This information benefits professionals, decision makers, and the public relying on the data provided by short-term case count estimates at varied geographic levels. METHODS We created 1-, 3-, and 7-day forecasts at the county, health district, and state levels using (1) a naïve approach, (2) Holt-Winters (HW) exponential smoothing, (3) a growth rate approach, (4) a moving average (MA) approach, (5) an autoregressive (AR) approach, (6) an autoregressive moving average (ARMA) approach, and (7) an autoregressive integrated moving average (ARIMA) approach. Forecasts relied on Virginia's 3464 historical county-level cumulative case counts from March 7 to April 22, 2020, as reported by The New York Times. Statistically significant results were identified using 95% CIs of median absolute error (MdAE) and median absolute percentage error (MdAPE) metrics of the resulting 216,698 forecasts. RESULTS The next-day MA forecast with 3-day look-back length obtained the lowest MdAE (median 0.67, 95% CI 0.49-0.84, P<.001) and statistically significantly differed from 39 out of 59 alternatives (66%) to 53 out of 59 alternatives (90%) at each geographic level at a significance level of .01. For short-range forecasting, methods assuming stationary means of prior days' counts outperformed methods with assumptions of weak stationarity or nonstationarity means. MdAPE results revealed statistically significant differences across geographic levels. CONCLUSIONS For short-range COVID-19 cumulative case count forecasting at the county, health district, and state levels during early onset, the following were found: (1) the MA method was effective for forecasting 1-, 3-, and 7-day cumulative case counts; (2) exponential growth was not the best representation of case growth during early virus onset when the public was aware of the virus; and (3) geographic resolution was a factor in the selection of forecasting methods.
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Affiliation(s)
- Christopher J Lynch
- Virginia Modeling, Analysis, and Simulation Center, Old Dominion University, Suffolk, VA, United States
| | - Ross Gore
- Virginia Modeling, Analysis, and Simulation Center, Old Dominion University, Suffolk, VA, United States
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34
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Wei Y, Wang J, Song W, Xiu C, Ma L, Pei T. Spread of COVID-19 in China: analysis from a city-based epidemic and mobility model. CITIES (LONDON, ENGLAND) 2021; 110:103010. [PMID: 33162634 PMCID: PMC7598765 DOI: 10.1016/j.cities.2020.103010] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 09/09/2020] [Accepted: 10/24/2020] [Indexed: 05/04/2023]
Abstract
Understanding the processes and mechanisms of the spatial spread of epidemics is essential for making reasonable judgments on the development trends of epidemics and for adopting effective containment measures. Using multi-agent network technology and big data on population migration, this paper constructed a city-based epidemic and mobility model (CEMM) to stimulate the spatiotemporal of COVID-19. Compared with traditional models, this model is characterized by an urban network perspective and emphasizes the important role of intercity population mobility and high-speed transportation networks. The results show that the model could simulate the inter-city spread of COVID-19 at the early stage in China with high precision. Through scenario simulation, the paper quantitatively evaluated the effect of control measures "city lockdown" and "decreasing population mobility" on containing the spatial spread of the COVID-19 epidemic. According to the simulation, the total number of infectious cases in China would have climbed to 138,824 on February 2020, or 4.46 times the real number, if neither of the measures had been implemented. Overall, the containment effect of the lockdown of cities in Hubei was greater than that of decreasing intercity population mobility, and the effect of city lockdowns was more sensitive to timing relative to decreasing population mobility.
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Affiliation(s)
- Ye Wei
- Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun, Jilin 130024, China
| | - Jiaoe Wang
- Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Song
- Department of Geography and Geosciences, University of Louisville, Louisville, KY, USA
| | - Chunliang Xiu
- College of Jang Ho Architecture, Northeastern University, Shenyang, Liaoning, China
| | - Li Ma
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Tao Pei
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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35
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Pei S, Teng X, Lewis P, Shaman J. Optimizing respiratory virus surveillance networks using uncertainty propagation. Nat Commun 2021; 12:222. [PMID: 33431854 PMCID: PMC7801666 DOI: 10.1038/s41467-020-20399-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 12/01/2020] [Indexed: 02/07/2023] Open
Abstract
Infectious disease prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for diseases with limited records. The proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens - human metapneumovirus and seasonal coronavirus - from 35 US states and can be used to guide systemic allocation of surveillance efforts.
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Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA.
| | - Xian Teng
- School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Paul Lewis
- Integrated Biosurveillance Section, Armed Forces Health Surveillance Branch, Silver Spring, MD, 20904, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA.
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Olisah CC, Ilori OO, Adelaja K, Usip PU, Uzoechi LO, Adeyanju IA, Odumuyiwa VT. Data-driven approach to COVID-19 infection forecast for Nigeria using negative binomial regression model. DATA SCIENCE FOR COVID-19 2021. [PMCID: PMC8137713 DOI: 10.1016/b978-0-12-824536-1.00002-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
COVID-19: the new wave of a global pandemic, is bringing about an increasing number of scientific efforts aimed at enabling governments to make informed decisions. In this paper, we explore the negative binomial regression model from the family of generalized linear models for the prediction of the future infection pattern of COVID-19 in Nigeria. We approached the prediction from a new perspective that is inspired by transfer learning and feature engineering approaches widely adopted in machine learning. We trained the model to learn COVID-19 pattern cues of other countries such as South Africa, Senegal, Slovenia, Australia, Belgium, and Israel with sufficient and recorded infection cases and test count as baseline data; and created additional features to increase the model's predictive power. With a testing capacity of 2000 persons per day in Nigeria, the cumulative infection counts for 30-04-2020, 15-05-2020, and 22-05-2020 were predicted to rise to 3044, 5622, and 7254 respectively.
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Sachak-Patwa R, Byrne HM, Thompson RN. Accounting for cross-immunity can improve forecast accuracy during influenza epidemics. Epidemics 2020; 34:100432. [PMID: 33360870 DOI: 10.1016/j.epidem.2020.100432] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 12/11/2020] [Accepted: 12/15/2020] [Indexed: 11/17/2022] Open
Abstract
Previous exposure to influenza viruses confers cross-immunity against future infections with related strains. However, this is not always accounted for explicitly in mathematical models used for forecasting during influenza outbreaks. We show that, if an influenza outbreak is due to a strain that is similar to one that has emerged previously, then accounting for cross-immunity explicitly can improve the accuracy of real-time forecasts. To do this, we consider two infectious disease outbreak forecasting models. In the first (the "1-group model"), all individuals are assumed to be identical and cross-immunity is not accounted for. In the second (the "2-group model"), individuals who have previously been infected by a related strain are assumed to be less likely to experience severe disease, and therefore recover more quickly, than immunologically naive individuals. We fit both models to estimated case notification data (including symptomatic individuals as well as laboratory-confirmed cases) from Japan from the 2009 H1N1 influenza pandemic, and then generate synthetic data for a future outbreak by assuming that the 2-group model represents the epidemiology of influenza infections more accurately. We use the 1-group model (as well as the 2-group model for comparison) to generate forecasts that would be obtained in real-time as the future outbreak is ongoing, using parameter values estimated from the 2009 epidemic as informative priors, motivated by the fact that without using prior information from 2009, the forecasts are highly uncertain. In the scenario that we consider, the 1-group model only produces accurate outbreak forecasts once the peak of the epidemic has passed, even when the values of important epidemiological parameters such as the lengths of the mean incubation and infectious periods are known exactly. As a result, it is necessary to use the more epidemiologically realistic 2-group model to generate accurate forecasts. Accounting for cross-immunity driven by exposures in previous outbreaks explicitly is expected to improve the accuracy of epidemiological modelling forecasts during influenza outbreaks.
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Affiliation(s)
- Rahil Sachak-Patwa
- Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
| | - Helen M Byrne
- Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK
| | - Robin N Thompson
- Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK; Christ Church, University of Oxford, St Aldates, Oxford, OX1 1DP, UK; Present address: Mathematics Institute, University of Warwick, Zeeman Building, Coventry, CV4 7AL, UK
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Zhong S, Bian L. What drives disease flows between locations? TRANSACTIONS IN GIS : TG 2020; 24:1740-1755. [PMID: 33343221 PMCID: PMC7745922 DOI: 10.1111/tgis.12675] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Communicable diseases 'flow' between locations. These flows dictate where and when certain communities will be affected. While the prediction of disease flows is essential for the timely intervention of epidemics, few studies have addressed this critical issue. This study predicts disease flows during an epidemic by considering the epidemiological, network, and temporal contextual factors using a deep learning approach. A series of scenario analyses helps identify the effects of these contextual factors on disease flows. Results show that the extended spatial-temporal effect of the epidemiological factors stimulates disease flows. The compound effects of the network factors enhance the transmission efficiency of these flows. Lastly, the temporal effect accelerates the combined effects of epidemiological and network factors on the flows. Findings of this study reveal the intricate nature of disease flows and lay a solid foundation for real-time surveillance of epidemics and pandemics to inform timely interventions for a broad range of communicable diseases.
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Affiliation(s)
- Shiran Zhong
- Department of Geography, University at Buffalo, the State University of New York, Buffalo, USA
| | - Ling Bian
- Department of Geography, University at Buffalo, the State University of New York, Buffalo, USA
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Silva AF, Tavakoli M. Domiciliary Hospitalization through Wearable Biomonitoring Patches: Recent Advances, Technical Challenges, and the Relation to Covid-19. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6835. [PMID: 33260466 PMCID: PMC7729497 DOI: 10.3390/s20236835] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/10/2020] [Accepted: 11/23/2020] [Indexed: 12/16/2022]
Abstract
This article reviews recent advances and existing challenges for the application of wearable bioelectronics for patient monitoring and domiciliary hospitalization. More specifically, we focus on technical challenges and solutions for the implementation of wearable and conformal bioelectronics for long-term patient biomonitoring and discuss their application on the Internet of medical things (IoMT). We first discuss the general architecture of IoMT systems for domiciliary hospitalization and the three layers of the system, including the sensing, communication, and application layers. In regard to the sensing layer, we focus on current trends, recent advances, and challenges in the implementation of stretchable patches. This includes fabrication strategies and solutions for energy storage and energy harvesting, such as printed batteries and supercapacitors. As a case study, we discuss the application of IoMT for domiciliary hospitalization of COVID 19 patients. This can be used as a strategy to reduce the pressure on the healthcare system, as it allows continuous patient monitoring and reduced physical presence in the hospital, and at the same time enables the collection of large data for posterior analysis. Finally, based on the previous works in the field, we recommend a conceptual IoMT design for wearable monitoring of COVID 19 patients.
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Affiliation(s)
| | - Mahmoud Tavakoli
- Institute of Systems and Robotics, Department of Electrical Engineering, University of Coimbra, 3030-290 Coimbra, Portugal;
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40
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Haw DJ, Pung R, Read JM, Riley S. Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering. Proc Natl Acad Sci U S A 2020; 117:23636-23642. [PMID: 32900923 PMCID: PMC7519285 DOI: 10.1073/pnas.1910181117] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Some directly transmitted human pathogens, such as influenza and measles, generate sustained exponential growth in incidence and have a high peak incidence consistent with the rapid depletion of susceptible individuals. Many do not. While a prolonged exponential phase typically arises in traditional disease-dynamic models, current quantitative descriptions of nonstandard epidemic profiles are either abstract, phenomenological, or rely on highly skewed offspring distributions in network models. Here, we create large socio-spatial networks to represent contact behavior using human population-density data, a previously developed fitting algorithm, and gravity-like mobility kernels. We define a basic reproductive number [Formula: see text] for this system, analogous to that used for compartmental models. Controlling for [Formula: see text], we then explore networks with a household-workplace structure in which between-household contacts can be formed with varying degrees of spatial correlation, determined by a single parameter from the gravity-like kernel. By varying this single parameter and simulating epidemic spread, we are able to identify how more frequent local movement can lead to strong spatial correlation and, thus, induce subexponential outbreak dynamics with lower, later epidemic peaks. Also, the ratio of peak height to final size was much smaller when movement was highly spatially correlated. We investigate the topological properties of our networks via a generalized clustering coefficient that extends beyond immediate neighborhoods, identifying very strong correlations between fourth-order clustering and nonstandard epidemic dynamics. Our results motivate the observation of both incidence and socio-spatial human behavior during epidemics that exhibit nonstandard incidence patterns.
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Affiliation(s)
- David J Haw
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, United Kingdom
| | - Rachael Pung
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, United Kingdom
| | - Jonathan M Read
- Centre for Health Informatics Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - Steven Riley
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W2 1PG, United Kingdom;
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Sambaturu P, Bhattacharya P, Chen J, Lewis B, Marathe M, Venkatramanan S, Vullikanti A. An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study. JMIR Public Health Surveill 2020; 6:e12842. [PMID: 32701458 PMCID: PMC7501584 DOI: 10.2196/12842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 06/16/2019] [Accepted: 04/03/2020] [Indexed: 11/26/2022] Open
Abstract
Background Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. Objective Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. Methods We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). Results We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. Conclusions Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.
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Affiliation(s)
| | | | - Jiangzhuo Chen
- University of Virginia, Charlottesville, VA, United States
| | - Bryan Lewis
- University of Virginia, Charlottesville, VA, United States
| | - Madhav Marathe
- University of Virginia, Charlottesville, VA, United States
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Ahmetolan S, Bilge AH, Demirci A, Peker-Dobie A, Ergonul O. What Can We Estimate From Fatality and Infectious Case Data Using the Susceptible-Infected-Removed (SIR) Model? A Case Study of Covid-19 Pandemic. Front Med (Lausanne) 2020; 7:556366. [PMID: 33015109 PMCID: PMC7494820 DOI: 10.3389/fmed.2020.556366] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 08/12/2020] [Indexed: 01/04/2023] Open
Abstract
The rapidly spreading Covid-19 that affected almost all countries, was first reported at the end of 2019. As a consequence of its highly infectious nature, countries all over the world have imposed extremely strict measures to control its spread. Since the earliest stages of this major pandemic, academics have done a huge amount of research in order to understand the disease, develop medication, vaccines and tests, and model its spread. Among these studies, a great deal of effort has been invested in the estimation of epidemic parameters in the early stage, for the countries affected by Covid-19, hence to predict the course of the epidemic but the variability of the controls over the course of the epidemic complicated the modeling processes. In this article, the determination of the basic reproduction number, the mean duration of the infectious period, the estimation of the timing of the peak of the epidemic wave is discussed using early phase data. Daily case reports and daily fatalities for China, South Korea, France, Germany, Italy, Spain, Iran, Turkey, the United Kingdom and the United States over the period January 22, 2020-April 18, 2020 are evaluated using the Susceptible-Infected-Removed (SIR) model. For each country, the SIR models fitting cumulative infective case data within 5% error are analyzed. It is observed that the basic reproduction number and the mean duration of the infectious period can be estimated only in cases where the spread of the epidemic is over (for China and South Korea in the present case). Nevertheless, it is shown that the timing of the maximum and timings of the inflection points of the proportion of infected individuals can be robustly estimated from the normalized data. The validation of the estimates by comparing the predictions with actual data has shown that the predictions were realized for all countries except USA, as long as lock-down measures were retained.
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Affiliation(s)
- Semra Ahmetolan
- Department of Mathematics, Faculty of Science and Letters, Istanbul Technical University, Istanbul, Turkey
| | - Ayse Humeyra Bilge
- Department of Industrial Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Turkey
| | - Ali Demirci
- Department of Mathematics, Faculty of Science and Letters, Istanbul Technical University, Istanbul, Turkey
| | - Ayse Peker-Dobie
- Department of Mathematics, Faculty of Science and Letters, Istanbul Technical University, Istanbul, Turkey
- Department of Industrial Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Turkey
| | - Onder Ergonul
- Infectious Diseases and Clinical Microbiology Department, School of Medicine, Koc University, Istanbul, Turkey
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43
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Hazarie S, Barbosa H, Frank A, Menezes R, Ghoshal G. Uncovering the differences and similarities between physical and virtual mobility. J R Soc Interface 2020; 17:20200250. [PMID: 32693745 DOI: 10.1098/rsif.2020.0250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The recent availability of digital traces from information and communications technologies has facilitated the study of both individual- and population-level movement with unprecedented spatio-temporal resolution, enabling us to better understand a plethora of socio-economic processes such as urbanization, transportation, impact on the environment and epidemic spreading to name a few. Using empirical spatio-temporal trends, several mobility models have been proposed to explain the observed regularities in human movement. With the advent of the World Wide Web, a new type of virtual mobility has emerged that has begun to supplant many traditional facets of human activity. Here, we conduct a systematic analysis of physical and virtual movement, uncovering both similarities and differences in their statistical patterns. The differences manifest themselves primarily in the temporal regime, as a signature of the spatial and economic constraints inherent in physical movement, features that are predominantly absent in the virtual space. We demonstrate that once one moves to the time-independent space of events, i.e. the sequences of visited locations, these differences vanish, and the statistical patterns of physical and virtual mobility are identical. The observed similarity in navigating these markedly different domains points towards a common mechanism governing the movement patterns, a feature we describe through a Metropolis-Hastings type optimization model, where individuals navigate locations through decision-making processes resembling a cost-benefit analysis of the utility of locations. In contrast to existing phenomenological models of mobility, we show that our model can reproduce the commonalities in the empirically observed statistics with minimal input.
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Affiliation(s)
- Surendra Hazarie
- Department of Physics and Astronomy, University of Rochester, Rochester, NY, USA
| | - Hugo Barbosa
- Department of Physics and Astronomy, University of Rochester, Rochester, NY, USA.,Department of Computer Science, University of Exeter, Exeter, UK
| | - Adam Frank
- Department of Physics and Astronomy, University of Rochester, Rochester, NY, USA
| | - Ronaldo Menezes
- Department of Computer Science, University of Exeter, Exeter, UK
| | - Gourab Ghoshal
- Department of Physics and Astronomy, University of Rochester, Rochester, NY, USA
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Siwiak M, Szczesny P, Siwiak M. From the index case to global spread: the global mobility based modelling of the COVID-19 pandemic implies higher infection rate and lower detection ratio than current estimates. PeerJ 2020; 8:e9548. [PMID: 32728498 PMCID: PMC7357567 DOI: 10.7717/peerj.9548] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 06/24/2020] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Since the outbreak of the COVID-19 pandemic, multiple efforts of modelling of the geo-temporal transmissibility of the virus have been undertaken, but none describes the pandemic spread at the global level. The aim of this research is to provide a high-resolution global model of the pandemic that overcomes the problem of biased country-level data on the number of infected cases. To achieve this we propose a novel SIR-type metapopulation transmission model and a set of analytically derived model parameters. We used them to perform a simulation of the disease spread with help of the Global Epidemic and Mobility (GLEAM) framework embedding actual population densities, commute patterns and long-range travel networks. The simulation starts on 17 November 2019 with the index case (presymptomatic, yet infectious) in Wuhan, China, and results in an accurate prediction of the number of diagnosed cases after 154 days in multiple countries across five continents. In addition, the model outcome shows high compliance with the results of a random screening test conducted on pregnant women in the New York area. METHODS We have built a modified SIR metapopulation transmission model and parameterized it analytically either by setting the values of the parameters based on the literature, or by assuming their plausible values. We compared our results with the number of diagnosed cases in twenty selected countries, ones which provide reliable statistics but differ substantially in terms of strength and speed of undertaken Non-Drug Interventions. The obtained 95% confidence intervals for the predictions are in agreement with the empirical data. RESULTS The parameters that successfully model the pandemic are: the basic reproduction number R 0, 4.4; a latent non-infectious period of 1.1. days followed by 4.6 days of the presymptomatic infectious period; the probability of developing severe symptoms, 0.01; the probability of being diagnosed when presenting severe symptoms of 0.6; the probability of diagnosis for cases with mild symptoms or asymptomatic, 0.001. DISCUSSION Parameters that successfully reproduce the observed number of cases indicate that both R 0 and the prevalence of the virus might be underestimated. This is in concordance with the newest research on undocumented COVID-19 cases. Consequently, the actual mortality rate is putatively lower than estimated. Confirmation of the pandemic characteristic by further refinement of the model and screening tests is crucial for developing an effective strategy for the global epidemiological crisis.
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Shinde GR, Kalamkar AB, Mahalle PN, Dey N, Chaki J, Hassanien AE. Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art. SN COMPUTER SCIENCE 2020; 1:197. [PMID: 33063048 PMCID: PMC7289234 DOI: 10.1007/s42979-020-00209-9] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 05/28/2020] [Indexed: 12/22/2022]
Abstract
COVID-19 is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients has been increasing at an alarming rate in almost all the affected nations. Forecasting techniques can be inculcated thereby assisting in designing better strategies and in taking productive decisions. These techniques assess the situations of the past thereby enabling better predictions about the situation to occur in the future. These predictions might help to prepare against possible threats and consequences. Forecasting techniques play a very important role in yielding accurate predictions. This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/machine learning techniques. Data collected from various platforms also play a vital role in forecasting. In this study, two categories of datasets have been discussed, i.e., big data accessed from World Health Organization/National databases and data from a social media communication. Forecasting of a pandemic can be done based on various parameters such as the impact of environmental factors, incubation period, the impact of quarantine, age, gender and many more. These techniques and parameters used for forecasting are extensively studied in this work. However, forecasting techniques come with their own set of challenges (technical and generic). This study discusses these challenges and also provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic.
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Affiliation(s)
- Gitanjali R. Shinde
- Department of Computer Engineering, Smt. Kashibai Navale College of Engineering, Pune, Maharashtra India
| | - Asmita B. Kalamkar
- Department of Computer Engineering, Smt. Kashibai Navale College of Engineering, Pune, Maharashtra India
| | - Parikshit N. Mahalle
- Department of Computer Engineering, Smt. Kashibai Navale College of Engineering, Pune, Maharashtra India
- Department of Communication, Media and Information Technologies, Aalborg University, Copenhagen, Denmark
| | - Nilanjan Dey
- Department of Information Technology, Techno International New Town, Kolkata, India
| | - Jyotismita Chaki
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Aboul Ella Hassanien
- Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt
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Abstract
Our understanding of the dynamics of complex networked systems has increased significantly in the last two decades. However, most of our knowledge is built upon assuming pairwise relations among the system's components. This is often an oversimplification, for instance, in social interactions that occur frequently within groups. To overcome this limitation, here we study the dynamics of social contagion on hypergraphs. We develop an analytical framework and provide numerical results for arbitrary hypergraphs, which we also support with Monte Carlo simulations. Our analyses show that the model has a vast parameter space, with first- and second-order transitions, bistability, and hysteresis. Phenomenologically, we also extend the concept of latent heat to social contexts, which might help understanding oscillatory social behaviors. Our work unfolds the research line of higher-order models and the analytical treatment of hypergraphs, posing new questions and paving the way for modeling dynamical processes on higher-order structures.
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Risk Prediction and Assessment: Duration, Infections, and Death Toll of the COVID-19 and Its Impact on China’s Economy. JOURNAL OF RISK AND FINANCIAL MANAGEMENT 2020. [DOI: 10.3390/jrfm13040066] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This study first analyzes the national and global infection status of the Coronavirus Disease that emerged in 2019 (COVID-19). It then uses the trend comparison method to predict the inflection point and Key Point of the COVID-19 virus by comparison with the severe acute respiratory syndrome (SARS) graphs, followed by using the Autoregressive Integrated Moving Average model, Autoregressive Moving Average model, Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors, and Holt Winter’s Exponential Smoothing to predict infections, deaths, and GDP in China. Finally, it discusses and assesses the impact of these results. This study argues that even if the risks and impacts of the epidemic are significant, China’s economy will continue to maintain steady development.
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48
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Ostwald Growth Rate in Controlled Covid-19 Epidemic Spreading as in Arrested Growth in Quantum Complex Matter. CONDENSED MATTER 2020. [DOI: 10.3390/condmat5020023] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Here, we focus on the data analysis of the growth of epidemic spread of Covid-19 in countries where different policies of containment were activated. It is known that the growth of pandemic spread at its threshold is exponential, but it is not known how to quantify the success of different containment policies. We identify that a successful approach gives an arrested phase regime following the Ostwald growth, where, over the course of time, one phase transforms into another metastable phase with a similar free energy as observed in oxygen interstitial diffusion in quantum complex matter and in crystallization of proteins. We introduce the s factor which provides a quantitative measure of the efficiency and speed of the adopted containment policy, which is very helpful not only to monitor the Covid-19 pandemic spread but also for other countries to choose the best containment policy. The results show that a policy based on joint confinement, targeted tests, and tracking positive cases is the most rapid pandemic containment policy; in fact, we found values of 9, 5, and 31 for the success s factor for China, South Korea, and Italy, respectively, where the lowest s factor indicates the best containment policy.
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49
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Reimering S, Muñoz S, McHardy AC. Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic. PLoS Comput Biol 2020; 16:e1007101. [PMID: 32032362 PMCID: PMC7032730 DOI: 10.1371/journal.pcbi.1007101] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 02/20/2020] [Accepted: 01/12/2020] [Indexed: 12/02/2022] Open
Abstract
Influenza A viruses cause seasonal epidemics and occasional pandemics in the human population. While the worldwide circulation of seasonal influenza is at least partly understood, the exact migration patterns between countries, states or cities are not well studied. Here, we use the Sankoff algorithm for parsimonious phylogeographic reconstruction together with effective distances based on a worldwide air transportation network. By first simulating geographic spread and then phylogenetic trees and genetic sequences, we confirmed that reconstructions with effective distances inferred phylogeographic spread more accurately than reconstructions with geographic distances and Bayesian reconstructions with BEAST that do not use any distance information, and led to comparable results to the Bayesian reconstruction using distance information via a generalized linear model. Our method extends Bayesian methods that estimate rates from the data by using fine-grained locations like airports and inferring intermediate locations not observed among sampled isolates. When applied to sequence data of the pandemic H1N1 influenza A virus in 2009, our approach correctly inferred the origin and proposed airports mainly involved in the spread of the virus. In case of a novel outbreak, this approach allows to rapidly analyze sequence data and infer origin and spread routes to improve disease surveillance and control. Influenza A viruses infect up to 5 million people in recurring epidemics every year. Further, viruses of zoonotic origin constantly pose a pandemic risk. Understanding the geographical spread of these viruses, including the origin and the main spread routes between cities, states or countries, could help to monitor or contain novel outbreaks. Based on genetic sequences and sampling locations, the geographic spread can be reconstructed along a phylogenetic tree. Our approach uses a parsimonious reconstruction with air transportation data and was verified using a simulation of the 2009 H1N1 influenza A pandemic. Applied to real sequence data of the outbreak, our analysis gave detailed insights into spread patterns of influenza A viruses, highlighting the origin as well as airports mainly involved in the spread.
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Affiliation(s)
- Susanne Reimering
- Department for Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany
| | - Sebastian Muñoz
- Department for Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany
| | - Alice C. McHardy
- Department for Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany
- German Center for Infection Research (DZIF), Braunschweig, Germany
- * E-mail:
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Predicting Seasonal Influenza Based on SARIMA Model, in Mainland China from 2005 to 2018. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16234760. [PMID: 31783697 PMCID: PMC6926639 DOI: 10.3390/ijerph16234760] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 11/24/2019] [Accepted: 11/25/2019] [Indexed: 11/17/2022]
Abstract
Seasonal influenza is one of the mandatorily monitored infectious diseases, in China. Making full use of the influenza surveillance data helps to predict seasonal influenza. In this study, a seasonal autoregressive integrated moving average (SARIMA) model was used to predict the influenza changes by analyzing monthly data of influenza incidence from January 2005 to December 2018, in China. The inter-annual incidence rate fluctuated from 2.76 to 55.07 per 100,000 individuals. The SARIMA (1, 0, 0) × (0, 1, 1) 12 model predicted that the influenza incidence in 2018 was similar to that of previous years, and it fitted the seasonal fluctuation. The relative errors between actual values and predicted values fluctuated from 0.0010 to 0.0137, which indicated that the predicted values matched the actual values well. This study demonstrated that the SARIMA model could effectively make short-term predictions of seasonal influenza.
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