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Cao W, Zhao S, Tong X, Dai H, Sun J, Xu J, Qiu G, Zhu J, Tian Y. Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:13086-13112. [PMID: 37501479 DOI: 10.3934/mbe.2023583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies.
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Affiliation(s)
- Wen Cao
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
| | - Siqi Zhao
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
| | - Xiaochong Tong
- School of Geospatial Information, University of Information Engineering, Zhengzhou 450001, China
| | - Haoran Dai
- Northern Information Control Research Institute Group Co. Ltd, Nanjing 211106, China
| | - Jiang Sun
- Beijing QTMap Technology Co. Ltd, Beijing 100192, China
| | - Jiaqi Xu
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
| | | | - Jingwen Zhu
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
| | - Yuzhen Tian
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
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2
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Liu P, Ikram R, Khan A, Din A. The measles epidemic model assessment under real statistics: an application of stochastic optimal control theory. Comput Methods Biomech Biomed Engin 2023; 26:138-159. [PMID: 35297714 DOI: 10.1080/10255842.2022.2050222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
A stochastic epidemic model with random noise transmission is taken into account, describing the dynamics of the measles viral infection. The basic reproductive number is calculated corresponding to the stochastic model. It is determined that, given initial positive data, the model has bounded, unique, and positive solution. Additionally, utilizing stochastic Lyapunov functional theory, we study the extinction of the disease. Stationary distribution and extinction of the infection are examined by providing sufficient conditions. We employed optimal control principles and examined stochastic control systems to regulate the transmission of the virus using environmental factors. Graphical representations have been offered for simplicity of comprehending in order to further verify the acquired analytical findings.
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Affiliation(s)
- Peijiang Liu
- School of Statistics and Mathematics, Guangdong University of Finance and Economics, Big data and Educational Statistics Application Laboratory, Guangzhou, PR China.,School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou, PR China
| | - Rukhsar Ikram
- Department of Mathematics and Statistics, University of Swat, Khyber Pakhtunkhwa, Pakistan
| | - Amir Khan
- Department of Mathematics, Faculty of Science, King Mongkut's University of Technology, Thonburi (KMUTT), Bangkok, Thailand
| | - Anwarud Din
- Department of Mathematics, Sun Yat-sen University, Guangzhou, PR China
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3
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Assessing the impact of human mobility to predict regional excess death in Ecuador. Sci Rep 2022; 12:370. [PMID: 35013374 PMCID: PMC8748783 DOI: 10.1038/s41598-021-03926-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 12/07/2021] [Indexed: 01/07/2023] Open
Abstract
COVID-19 outbreaks have had high mortality in low- and middle-income countries such as Ecuador. Human mobility is an important factor influencing the spread of diseases possibly leading to a high burden of disease at the country level. Drastic control measures, such as complete lockdown, are effective epidemic controls, yet in practice one hopes that a partial shutdown would suffice. It is an open problem to determine how much mobility can be allowed while controlling an outbreak. In this paper, we use statistical models to relate human mobility to the excess death in Ecuador while controlling for demographic factors. The mobility index provided by GRANDATA, based on mobile phone users, represents the change of number of out-of-home events with respect to a benchmark date (March 2nd, 2020). The study confirms the global trend that more men are dying than expected compared to women, and that people under 30 show less deaths than expected, particularly individuals younger than 20 with a death rate reduction between 22 and 27%. The weekly median mobility time series shows a sharp decrease in human mobility immediately after a national lockdown was declared on March 17, 2020 and a progressive increase towards the pre-lockdown level within two months. Relating median mobility to excess deaths shows a lag in its effect: first, a decrease in mobility in the previous two to three weeks decreases excess death and, more novel, we found an increase of mobility variability four weeks prior increases the number of excess deaths.
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Metcalf CJE, Andriamandimby SF, Baker RE, Glennon EE, Hampson K, Hollingsworth TD, Klepac P, Wesolowski A. Challenges in evaluating risks and policy options around endemic establishment or elimination of novel pathogens. Epidemics 2021; 37:100507. [PMID: 34823222 PMCID: PMC7612525 DOI: 10.1016/j.epidem.2021.100507] [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] [Received: 05/17/2021] [Revised: 08/20/2021] [Accepted: 10/06/2021] [Indexed: 11/12/2022] Open
Abstract
When a novel pathogen emerges there may be opportunities to eliminate transmission - locally or globally - whilst case numbers are low. However, the effort required to push a disease to elimination may come at a vast cost at a time when uncertainty is high. Models currently inform policy discussions on this question, but there are a number of open challenges, particularly given unknown aspects of the pathogen biology, the effectiveness and feasibility of interventions, and the intersecting political, economic, sociological and behavioural complexities for a novel pathogen. In this overview, we detail how models might identify directions for better leveraging or expanding the scope of data available on the pathogen trajectory, for bounding the theoretical context of emergence relative to prospects for elimination, and for framing the larger economic, behavioural and social context that will influence policy decisions and the pathogen’s outcome.
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Affiliation(s)
- C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Princeton School of Public and International Affairs, Princeton University, Princeton, USA.
| | | | - Rachel E Baker
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Princeton High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
| | - Emma E Glennon
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK
| | - Katie Hampson
- Institute of Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Glasgow, UK
| | - T Deirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | - Petra Klepac
- London School of Hygiene and Tropical Medicine, London, UK
| | - Amy Wesolowski
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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5
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Glennon EE, Bruijning M, Lessler J, Miller IF, Rice BL, Thompson RN, Wells K, Metcalf CJE. Challenges in modeling the emergence of novel pathogens. Epidemics 2021; 37:100516. [PMID: 34775298 DOI: 10.1016/j.epidem.2021.100516] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/29/2021] [Accepted: 10/22/2021] [Indexed: 01/24/2023] Open
Abstract
The emergence of infectious agents with pandemic potential present scientific challenges from detection to data interpretation to understanding determinants of risk and forecasts. Mathematical models could play an essential role in how we prepare for future emergent pathogens. Here, we describe core directions for expansion of the existing tools and knowledge base, including: using mathematical models to identify critical directions and paths for strengthening data collection to detect and respond to outbreaks of novel pathogens; expanding basic theory to identify infectious agents and contexts that present the greatest risks, over both the short and longer term; by strengthening estimation tools that make the most use of the likely range and uncertainties in existing data; and by ensuring modelling applications are carefully communicated and developed within diverse and equitable collaborations for increased public health benefit.
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Affiliation(s)
- Emma E Glennon
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK.
| | - Marjolein Bruijning
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Justin Lessler
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Ian F Miller
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA; Rocky Mountain Biological Laboratory, Crested Butte, CO 81224, USA
| | - Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA; Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar
| | - Robin N Thompson
- Mathematics Institute, University of Warwick, Warwick CV4 7AL, UK; The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Warwick CV4 7AL, UK
| | - Konstans Wells
- Department of Biosciences, Swansea University, Swansea SA28PP, UK
| | - C Jessica E Metcalf
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK; Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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6
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Qureshi S. Periodic dynamics of rubella epidemic under standard and fractional Caputo operator with real data from Pakistan. MATHEMATICS AND COMPUTERS IN SIMULATION 2020. [DOI: 10.1016/j.matcom.2020.06.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
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7
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Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data. Proc Natl Acad Sci U S A 2020; 117:28506-28514. [PMID: 33106403 DOI: 10.1073/pnas.2011529117] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
The United States experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination rates above the World Health Organization recommendation of 95% coverage with two doses. Since the COVID-19 pandemic began, resulting in suspension of many clinical preventive services, pediatric vaccination rates in the United States have fallen precipitously, dramatically increasing risk of measles resurgence. Previous research has shown that measles outbreaks in high-coverage contexts are driven by spatial clustering of nonvaccination, which decreases local immunity below the herd immunity threshold. However, little is known about how to best conduct surveillance and target interventions to detect and address these high-risk areas, and most vaccination data are reported at the state-level-a resolution too coarse to detect community-level clustering of nonvaccination characteristic of recent outbreaks. In this paper, we perform a series of computational experiments to assess the impact of clustered nonvaccination on outbreak potential and magnitude of bias in predicting disease risk posed by measuring vaccination rates at coarse spatial scales. We find that, when nonvaccination is locally clustered, reporting aggregate data at the state- or county-level can result in substantial underestimates of outbreak risk. The COVID-19 pandemic has shone a bright light on the weaknesses in US infectious disease surveillance and a broader gap in our understanding of how to best use detailed spatial data to interrupt and control infectious disease transmission. Our research clearly outlines that finer-scale vaccination data should be collected to prevent a return to endemic measles transmission in the United States.
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8
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Grantz KH, Meredith HR, Cummings DAT, Metcalf CJE, Grenfell BT, Giles JR, Mehta S, Solomon S, Labrique A, Kishore N, Buckee CO, Wesolowski A. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nat Commun 2020; 11:4961. [PMID: 32999287 PMCID: PMC7528106 DOI: 10.1038/s41467-020-18190-5] [Citation(s) in RCA: 160] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 08/06/2020] [Indexed: 11/24/2022] Open
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic has heightened discussion of the use of mobile phone data in outbreak response. Mobile phone data have been proposed to monitor effectiveness of non-pharmaceutical interventions, to assess potential drivers of spatiotemporal spread, and to support contact tracing efforts. While these data may be an important part of COVID-19 response, their use must be considered alongside a careful understanding of the behaviors and populations they capture. Here, we review the different applications for mobile phone data in guiding and evaluating COVID-19 response, the relevance of these applications for infectious disease transmission and control, and potential sources and implications of selection bias in mobile phone data. We also discuss best practices and potential pitfalls for directly integrating the collection, analysis, and interpretation of these data into public health decision making.
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Affiliation(s)
- Kyra H Grantz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Hannah R Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Derek A T Cummings
- Department of Biology and the Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology and the Woodrow Wilson School of International and Public Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology and the Woodrow Wilson School of International and Public Affairs, Princeton University, Princeton, NJ, USA
| | - John R Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Shruti Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sunil Solomon
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Alain Labrique
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Nishant Kishore
- Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Caroline O Buckee
- Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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9
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Giles JR, Zu Erbach-Schoenberg E, Tatem AJ, Gardner L, Bjørnstad ON, Metcalf CJE, Wesolowski A. The duration of travel impacts the spatial dynamics of infectious diseases. Proc Natl Acad Sci U S A 2020; 117:22572-22579. [PMID: 32839329 PMCID: PMC7486699 DOI: 10.1073/pnas.1922663117] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on human-mobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogen-generation time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions.
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Affiliation(s)
- John R Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;
| | - Elisabeth Zu Erbach-Schoenberg
- Department of Geography and the Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom
- WorldPop, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Andrew J Tatem
- Department of Geography and the Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom
- WorldPop, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Lauren Gardner
- Department of Civil and Systems Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, MD 21218
| | - Ottar N Bjørnstad
- Department of Entomology, Pennsylvania State University, University Park, PA 16802
| | - C J E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08544
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
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10
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Cutts FT, Dansereau E, Ferrari MJ, Hanson M, McCarthy KA, Metcalf CJE, Takahashi S, Tatem AJ, Thakkar N, Truelove S, Utazi E, Wesolowski A, Winter AK. Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications. Vaccine 2020; 38:979-992. [PMID: 31787412 PMCID: PMC6996156 DOI: 10.1016/j.vaccine.2019.11.020] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 11/07/2019] [Accepted: 11/08/2019] [Indexed: 01/30/2023]
Abstract
After many decades of vaccination, measles epidemiology varies greatly between and within countries. National immunization programs are therefore encouraged to conduct regular situation analyses and to leverage models to adapt interventions to local needs. Here, we review applications of models to develop locally tailored interventions to support control and elimination efforts. In general, statistical and semi-mechanistic transmission models can be used to synthesize information from vaccination coverage, measles incidence, demographic, and/or serological data, offering a means to estimate the spatial and age-specific distribution of measles susceptibility. These estimates complete the picture provided by vaccination coverage alone, by accounting for natural immunity. Dynamic transmission models can then be used to evaluate the relative impact of candidate interventions for measles control and elimination and the expected future epidemiology. In most countries, models predict substantial numbers of susceptible individuals outside the age range of routine vaccination, which affects outbreak risk and necessitates additional intervention to achieve elimination. More effective use of models to inform both vaccination program planning and evaluation requires the development of training to enhance broader understanding of models and where feasible, building capacity for modelling in-country, pipelines for rapid evaluation of model predictions using surveillance data, and clear protocols for incorporating model results into decision-making.
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Affiliation(s)
- F T Cutts
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| | - E Dansereau
- Vaccine Delivery, Global Development, The Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - M J Ferrari
- Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
| | - M Hanson
- Vaccine Delivery, Global Development, The Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - K A McCarthy
- Institute for Disease Modeling, 3150 139th Ave SE, Bellevue, WA 98005, USA
| | - C J E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - S Takahashi
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Department of Medicine, University of California San Francisco, San Francisco, CA 94143, USA
| | - A J Tatem
- WorldPop, Department of Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - N Thakkar
- Institute for Disease Modeling, 3150 139th Ave SE, Bellevue, WA 98005, USA
| | - S Truelove
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - E Utazi
- WorldPop, Department of Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - A Wesolowski
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - A K Winter
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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11
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Memon Z, Qureshi S, Memon BR. Mathematical analysis for a new nonlinear measles epidemiological system using real incidence data from Pakistan. EUROPEAN PHYSICAL JOURNAL PLUS 2020; 135:378. [PMID: 32435550 PMCID: PMC7223692 DOI: 10.1140/epjp/s13360-020-00392-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 04/07/2020] [Indexed: 05/03/2023]
Abstract
Modeling of infectious diseases is essential to comprehend dynamic behavior for the transmission of an epidemic. This research study consists of a newly proposed mathematical system for transmission dynamics of the measles epidemic. The measles system is based upon mass action principle wherein human population is divided into five mutually disjoint compartments: susceptible S(t)-vaccinated V(t)-exposed E(t)-infectious I(t)-recovered R(t). Using real measles cases reported from January 2019 to October 2019 in Pakistan, the system has been validated. Two unique equilibria called measles-free and endemic (measles-present) are shown to be locally asymptotically stable for basic reproductive number R 0 < 1 and R 0 > 1 , respectively. While using Lyapunov functions, the equilibria are found to be globally asymptotically stable under the former conditions on R 0 . However, backward bifurcation shows coexistence of stable endemic equilibrium with a stable measles-free equilibrium for R 0 < 1 . A strategy for measles control based on herd immunity is presented. The forward sensitivity indices for R 0 are also computed with respect to the estimated and fitted biological parameters. Finally, numerical simulations exhibit dynamical behavior of the measles system under influence of its parameters which further suggest improvement in both the vaccine efficacy and its coverage rate for substantial reduction in the measles epidemic.
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Affiliation(s)
- Zaibunnisa Memon
- Department of Basic Sciences and Related Studies, Mehran University of Engineering and Technology, Jamshoro, Sindh 76062 Pakistan
| | - Sania Qureshi
- Department of Basic Sciences and Related Studies, Mehran University of Engineering and Technology, Jamshoro, Sindh 76062 Pakistan
| | - Bisharat Rasool Memon
- Institute of Information and Communication Technology, University of Sindh, Jamshoro, Pakistan
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12
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A Fractional Measles Model Having Monotonic Real Statistical Data for Constant Transmission Rate of the Disease. FRACTAL AND FRACTIONAL 2019. [DOI: 10.3390/fractalfract3040053] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Non-Markovian effects have a vital role in modeling the processes related with natural phenomena such as epidemiology. Various infectious diseases have long-range memory characteristics and, thus, non-local operators are one of the best choices to be used to understand the transmission dynamics of such diseases and epidemics. In this paper, we study a fractional order epidemiological model of measles. Some relevant features, such as well-posedness and stability of the underlying Cauchy problem, are considered accompanying the proofs for a locally asymptotically stable equilibrium point for basic reproduction number R 0 < 1 , which is most sensitive to the fractional order parameter and to the percentage of vaccination. We show the efficiency of the model through a real life application of the spread of the epidemic in Pakistan, comparing the fractional and classical models, while assuming constant transmission rate of the epidemic with monotonically increasing and decreasing behavior of the infected population. Secondly, the fractional Caputo type model, based upon nonlinear least squares curve fitting technique, is found to have smaller residuals when compared with the classical model.
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13
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Lai S, Farnham A, Ruktanonchai NW, Tatem AJ. Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine. J Travel Med 2019; 26:taz019. [PMID: 30869148 PMCID: PMC6904325 DOI: 10.1093/jtm/taz019] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 03/08/2019] [Accepted: 03/08/2019] [Indexed: 11/15/2022]
Abstract
RATIONALE FOR REVIEW The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data. KEY FINDINGS Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel. CONCLUSIONS Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin-destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.
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Affiliation(s)
- Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- Flowminder Foundation, SE Stockholm, Sweden
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Dongan Road, Shanghai, China
| | - Andrea Farnham
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- Department of Public Health, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Nick W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- Flowminder Foundation, SE Stockholm, Sweden
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- Flowminder Foundation, SE Stockholm, Sweden
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