1
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Chae MK, Hwang DU, Nah K, Son WS. Evaluation of COVID-19 intervention policies in South Korea using the stochastic individual-based model. Sci Rep 2023; 13:18945. [PMID: 37919389 PMCID: PMC10622523 DOI: 10.1038/s41598-023-46277-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/30/2023] [Indexed: 11/04/2023] Open
Abstract
The COVID-19 pandemic has swept the globe, and countries have responded with various intervention policies to prevent its spread. In this study, we aim to analyze the effectiveness of intervention policies implemented in South Korea. We use a stochastic individual-based model (IBM) with a synthetic population to simulate the spread of COVID-19. Using statistical data, we make the synthetic population and assign sociodemographic attributes to each individual. Individuals go about their daily lives based on their assigned characteristics, and encountering infectors in their daily lives stochastically determines whether they are infected. We reproduce the transmission of COVID-19 using the IBM simulation from November 2020 to February 2021 when three phases of increasingly stringent intervention policies were implemented, and then assess their effectiveness. Additionally, we predict how the spread of infection would have been different if these policies had been implemented in January 2022. This study offers valuable insights into the effectiveness of intervention policies in South Korea, which can assist policymakers and public health officials in their decision-making process.
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Affiliation(s)
- Min-Kyung Chae
- Research Team for Transmission Dynamics of Infectious Diseases, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea
| | - Dong-Uk Hwang
- Research Team for Transmission Dynamics of Infectious Diseases, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea
| | - Kyeongah Nah
- Busan Center for Medical Mathematics, National Institute for Mathematical Sciences, Busan, 49241, Republic of Korea
| | - Woo-Sik Son
- Research Team for Transmission Dynamics of Infectious Diseases, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea.
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2
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Wang X, Wang H, Ramazi P, Nah K, Lewis M. A Hypothesis-Free Bridging of Disease Dynamics and Non-pharmaceutical Policies. Bull Math Biol 2022; 84:57. [PMID: 35394257 PMCID: PMC8991680 DOI: 10.1007/s11538-022-01012-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 03/08/2022] [Indexed: 11/22/2022]
Abstract
Accurate prediction of the number of daily or weekly confirmed cases of COVID-19 is critical to the control of the pandemic. Existing mechanistic models nicely capture the disease dynamics. However, to forecast the future, they require the transmission rate to be known, limiting their prediction power. Typically, a hypothesis is made on the form of the transmission rate with respect to time. Yet the real form is too complex to be mechanistically modeled due to the unknown dynamics of many influential factors. We tackle this problem by using a hypothesis-free machine-learning algorithm to estimate the transmission rate from data on non-pharmaceutical policies, and in turn forecast the confirmed cases using a mechanistic disease model. More specifically, we build a hybrid model consisting of a mechanistic ordinary differential equation (ODE) model and a gradient boosting model (GBM). To calibrate the parameters, we develop an “inverse method” that obtains the transmission rate inversely from the other variables in the ODE model and then feed it into the GBM to connect with the policy data. The resulting model forecasted the number of daily confirmed cases up to 35 days in the future in the USA with an averaged mean absolute percentage error of 27%. It can identify the most informative predictive variables, which can be helpful in designing improved forecasters as well as informing policymakers.
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Affiliation(s)
- Xiunan Wang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, T6G 2G1, Canada.,Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN, 37403, USA
| | - Hao Wang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, T6G 2G1, Canada.
| | - Pouria Ramazi
- Department of Mathematics and Statistics, Brock University, St. Catharines, ON, L2S 3A1, Canada
| | - Kyeongah Nah
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, T6G 2G1, Canada.,National Institute for Mathematical Sciences, Daejeon, 34047, Korea
| | - Mark Lewis
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, T6G 2G1, Canada.,Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2G1, Canada
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3
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Nah K, Wu J. Long-term transmission dynamics of tick-borne diseases involving seasonal variation and co-feeding transmission. J Biol Dyn 2021; 15:269-286. [PMID: 33905296 DOI: 10.1080/17513758.2021.1919322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 04/08/2021] [Indexed: 06/12/2023]
Abstract
Co-feeding is a mode of pathogen transmission for a wide range of tick-borne diseases where susceptible ticks can acquire infection from co-feeding with infected ticks on the same hosts. The significance of this transmission pathway is determined by the co-occurrence of ticks at different stages in the same season. Taking this into account, we formulate a system of differential equations with tick population dynamics and pathogen transmission dynamics highly regulated by the seasonal temperature variations. We examine the global dynamics of the model systems, and show that the two important ecological and epidemiological basic reproduction numbers can be used to fully characterize the long-term dynamics, and we link these two important threshold values to efficacy of co-feeding transmission.
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Affiliation(s)
- Kyeongah Nah
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- National Institute for Mathematical Sciences, Daejeon, Korea
| | - Jianhong Wu
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, Ontario, Canada
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4
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McCarthy Z, Xiao Y, Scarabel F, Tang B, Bragazzi NL, Nah K, Heffernan JM, Asgary A, Murty VK, Ogden NH, Wu J. Quantifying the shift in social contact patterns in response to non-pharmaceutical interventions. J Math Ind 2020; 10:28. [PMID: 33282625 PMCID: PMC7707617 DOI: 10.1186/s13362-020-00096-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 11/25/2020] [Indexed: 05/03/2023]
Abstract
Social contact mixing plays a critical role in influencing the transmission routes of infectious diseases. Moreover, quantifying social contact mixing patterns and their variations in a rapidly evolving pandemic intervened by changing public health measures is key for retroactive evaluation and proactive assessment of the effectiveness of different age- and setting-specific interventions. Contact mixing patterns have been used to inform COVID-19 pandemic public health decision-making; but a rigorously justified methodology to identify setting-specific contact mixing patterns and their variations in a rapidly developing pandemic, which can be informed by readily available data, is in great demand and has not yet been established. Here we fill in this critical gap by developing and utilizing a novel methodology, integrating social contact patterns derived from empirical data with a disease transmission model, that enables the usage of age-stratified incidence data to infer age-specific susceptibility, daily contact mixing patterns in workplace, household, school and community settings; and transmission acquired in these settings under different physical distancing measures. We demonstrated the utility of this methodology by performing an analysis of the COVID-19 epidemic in Ontario, Canada. We quantified the age- and setting (household, workplace, community, and school)-specific mixing patterns and their evolution during the escalation of public health interventions in Ontario, Canada. We estimated a reduction in the average individual contact rate from 12.27 to 6.58 contacts per day, with an increase in household contacts, following the implementation of control measures. We also estimated increasing trends by age in both the susceptibility to infection by SARS-CoV-2 and the proportion of symptomatic individuals diagnosed. Inferring the age- and setting-specific social contact mixing and key age-stratified epidemiological parameters, in the presence of evolving control measures, is critical to inform decision- and policy-making for the current COVID-19 pandemic.
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Affiliation(s)
- Zachary McCarthy
- Fields-CQAM Laboratory of Mathematics for Public Health (MfPH), York University, Toronto, Ontario Canada
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario Canada
| | - Yanyu Xiao
- Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH USA
| | - Francesca Scarabel
- Fields-CQAM Laboratory of Mathematics for Public Health (MfPH), York University, Toronto, Ontario Canada
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario Canada
- CDLab—Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
| | - Biao Tang
- Fields-CQAM Laboratory of Mathematics for Public Health (MfPH), York University, Toronto, Ontario Canada
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario Canada
| | - Nicola Luigi Bragazzi
- Fields-CQAM Laboratory of Mathematics for Public Health (MfPH), York University, Toronto, Ontario Canada
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario Canada
| | - Kyeongah Nah
- Fields-CQAM Laboratory of Mathematics for Public Health (MfPH), York University, Toronto, Ontario Canada
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario Canada
| | - Jane M. Heffernan
- Modelling Infection and Immunity Lab, Centre for Disease Modelling, Department of Mathematics and Statistics, York University, Toronto, Ontario Canada
| | - Ali Asgary
- Disaster & Emergency Management, School of Administrative Studies & Advanced Disaster & Emergency Rapid-Response Simulation (ADERSIM), York University, Toronto, Ontario Canada
| | - V. Kumar Murty
- Department of Mathematics, University of Toronto, Toronto, Ontario Canada
- The Fields Institute for Research in Mathematical Sciences, Toronto, Ontario Canada
| | - Nicholas H. Ogden
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, Quebec Canada
| | - Jianhong Wu
- Fields-CQAM Laboratory of Mathematics for Public Health (MfPH), York University, Toronto, Ontario Canada
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario Canada
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5
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Tosato M, Nah K, Wu J. Are host control strategies effective to eradicate tick-borne diseases (TBD)? J Theor Biol 2020; 508:110483. [PMID: 32918921 DOI: 10.1016/j.jtbi.2020.110483] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/31/2020] [Accepted: 09/02/2020] [Indexed: 11/29/2022]
Abstract
Ticks are responsible for spreading harmful diseases including Lyme disease and tick-borne encephalitis. Understanding tick population dynamics and predicting risk of tick-borne disease insurgence helps to design preventive actions against the disease spread. Using a compartmental model describing the pathogen transmission among ticks and hosts, we study the influence of host-targeted tick control strategies with chemical insecticides on tick population and disease transmission dynamics. Our analysis shows that in areas with rapid-growing population of ticks, host-targeted controls using chemical insecticides may enhance disease persistence and even turn a disease-free area to a disease endemic area. Therefore, the complex dynamics of pathogen spread among ticks and hosts should be carefully examined when designing tick control strategies.
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Affiliation(s)
- Marco Tosato
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario, Canada.
| | - Kyeongah Nah
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario, Canada
| | - Jianhong Wu
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, Ontario, Canada
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6
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McCarthy Z, Athar S, Alavinejad M, Chow C, Moyles I, Nah K, Kong JD, Agrawal N, Jaber A, Keane L, Liu S, Nahirniak M, Jean DS, Romanescu R, Stockdale J, Seet BT, Coudeville L, Thommes E, Taurel AF, Lee J, Shin T, Arino J, Heffernan J, Chit A, Wu J. Quantifying the annual incidence and underestimation of seasonal influenza: A modelling approach. Theor Biol Med Model 2020; 17:11. [PMID: 32646444 PMCID: PMC7347407 DOI: 10.1186/s12976-020-00129-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Accepted: 05/28/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Seasonal influenza poses a significant public health and economic burden, associated with the outcome of infection and resulting complications. The true burden of the disease is difficult to capture due to the wide range of presentation, from asymptomatic cases to non-respiratory complications such as cardiovascular events, and its seasonal variability. An understanding of the magnitude of the true annual incidence of influenza is important to support prevention and control policy development and to evaluate the impact of preventative measures such as vaccination. METHODS We use a dynamic disease transmission model, laboratory-confirmed influenza surveillance data, and randomized-controlled trial (RCT) data to quantify the underestimation factor, expansion factor, and symptomatic influenza illnesses in the US and Canada during the 2011-2012 and 2012-2013 influenza seasons. RESULTS Based on 2 case definitions, we estimate between 0.42-3.2% and 0.33-1.2% of symptomatic influenza illnesses were laboratory-confirmed in Canada during the 2011-2012 and 2012-2013 seasons, respectively. In the US, we estimate between 0.08-0.61% and 0.07-0.33% of symptomatic influenza illnesses were laboratory-confirmed in the 2011-2012 and 2012-2013 seasons, respectively. We estimated the symptomatic influenza illnesses in Canada to be 0.32-2.4 million in 2011-2012 and 1.8-8.2 million in 2012-2013. In the US, we estimate the number of symptomatic influenza illnesses to be 4.4-34 million in 2011-2012 and 23-102 million in 2012-2013. CONCLUSIONS We illustrate that monitoring a representative group within a population may aid in effectively modelling the transmission of infectious diseases such as influenza. In particular, the utilization of RCTs in models may enhance the accuracy of epidemiological parameter estimation.
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Affiliation(s)
- Zachary McCarthy
- Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada.,Laboratory for Industrial and Applied Mathematics, York University, Toronto, M3J 1P3, ON, Canada.,Centre for Disease Modelling, York University, Toronto, M3J 1P3, ON, Canada.,Fields-CQAM Mathematics for Public Health Laboratory, York University, Toronto, M3J 1P3, ON, Canada
| | - Safia Athar
- Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada.,Laboratory for Industrial and Applied Mathematics, York University, Toronto, M3J 1P3, ON, Canada.,Centre for Disease Modelling, York University, Toronto, M3J 1P3, ON, Canada.,Fields-CQAM Mathematics for Public Health Laboratory, York University, Toronto, M3J 1P3, ON, Canada
| | - Mahnaz Alavinejad
- Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada.,Laboratory for Industrial and Applied Mathematics, York University, Toronto, M3J 1P3, ON, Canada.,Centre for Disease Modelling, York University, Toronto, M3J 1P3, ON, Canada.,Fields-CQAM Mathematics for Public Health Laboratory, York University, Toronto, M3J 1P3, ON, Canada
| | - Christopher Chow
- Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada.,Laboratory for Industrial and Applied Mathematics, York University, Toronto, M3J 1P3, ON, Canada.,Centre for Disease Modelling, York University, Toronto, M3J 1P3, ON, Canada.,Fields-CQAM Mathematics for Public Health Laboratory, York University, Toronto, M3J 1P3, ON, Canada
| | - Iain Moyles
- Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada
| | - Kyeongah Nah
- Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada.,Laboratory for Industrial and Applied Mathematics, York University, Toronto, M3J 1P3, ON, Canada.,Centre for Disease Modelling, York University, Toronto, M3J 1P3, ON, Canada.,Fields-CQAM Mathematics for Public Health Laboratory, York University, Toronto, M3J 1P3, ON, Canada
| | - Jude D Kong
- Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada.,Laboratory for Industrial and Applied Mathematics, York University, Toronto, M3J 1P3, ON, Canada.,Centre for Disease Modelling, York University, Toronto, M3J 1P3, ON, Canada.,Fields-CQAM Mathematics for Public Health Laboratory, York University, Toronto, M3J 1P3, ON, Canada
| | | | - Ahmed Jaber
- Department of Mathematics, Simon Fraser University, Burnaby, V5A 1S6, BC, Canada
| | - Laura Keane
- Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada
| | - Sam Liu
- McMaster University, Hamilton, L8S 4L8, ON, Canada
| | - Myles Nahirniak
- Department of Mathematics, Simon Fraser University, Burnaby, V5A 1S6, BC, Canada
| | - Danielle St Jean
- Department of Mathematics, Simon Fraser University, Burnaby, V5A 1S6, BC, Canada
| | - Razvan Romanescu
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, M5G 1X5, ON, Canada
| | - Jessica Stockdale
- Department of Mathematics, Simon Fraser University, Burnaby, V5A 1S6, BC, Canada
| | - Bruce T Seet
- Sanofi Pasteur, Toronto, M2R 3T4, Canada.,Department of Molecular Genetics, Toronto, M5S 1A8, ON, Canada
| | | | - Edward Thommes
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, M3J 1P3, ON, Canada.,Department of Mathematics, Simon Fraser University, Burnaby, V5A 1S6, BC, Canada.,Sanofi Pasteur, Toronto, M2R 3T4, Canada
| | | | - Jason Lee
- Sanofi Pasteur, Toronto, M2R 3T4, Canada
| | | | - Julien Arino
- University of Manitoba, Department of Mathematics, Winnipeg, R3T 2N2, MB, Canada
| | - Jane Heffernan
- Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada.,Laboratory for Industrial and Applied Mathematics, York University, Toronto, M3J 1P3, ON, Canada.,Centre for Disease Modelling, York University, Toronto, M3J 1P3, ON, Canada.,Fields-CQAM Mathematics for Public Health Laboratory, York University, Toronto, M3J 1P3, ON, Canada
| | - Ayman Chit
- Leslie Dan School of Pharmacy, University of Toronto, Toronto, M5S 3M2, ON, Canada.,Sanofi Pasteur, Swiftwater, 18370, PA, USA
| | - Jianhong Wu
- Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada. .,Laboratory for Industrial and Applied Mathematics, York University, Toronto, M3J 1P3, ON, Canada. .,Centre for Disease Modelling, York University, Toronto, M3J 1P3, ON, Canada. .,Fields-CQAM Mathematics for Public Health Laboratory, York University, Toronto, M3J 1P3, ON, Canada.
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7
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Nah K, Alavinejad M, Rahman A, Heffernan JM, Wu J. Impact of influenza vaccine-modified infectivity on attack rate, case fatality ratio and mortality. J Theor Biol 2020; 492:110190. [PMID: 32035827 DOI: 10.1016/j.jtbi.2020.110190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 10/03/2019] [Accepted: 02/02/2020] [Indexed: 10/25/2022]
Abstract
Generally, vaccines are designed to provide protection against infection (susceptibility), disease (symptoms and transmissibility), and/or complications. In a recent study of influenza vaccination, it was observed that vaccinated yet infected individuals experienced increased transmission levels. In this paper, using a mathematical model of infection and transmission, we study the impact of vaccine-modified effects, including susceptibility and infectivity, on important epidemiological outcomes of an immunization program. The balance between vaccine-modified susceptibility, infectivity and recovery needed in preventing an influenza outbreak, or in mitigating the health outcomes of the outbreak is studied using the SIRV-type of disease transmission model. We also investigate the impact of influenza vaccination program on the infection risk of vaccinated and non-vaccinated individuals.
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Affiliation(s)
- Kyeongah Nah
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON M3J 1P3, Canada; Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
| | - Mahnaz Alavinejad
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON M3J 1P3, Canada; Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
| | - Ashrafur Rahman
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON M3J 1P3, Canada; Department of Mathematics and Statistics, Oakland University, Rochester, MI 48309, USA
| | - Jane M Heffernan
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada; Centre for Disease Modelling (CDM), York University, Toronto, ON M3J 1P3, Canada
| | - Jianhong Wu
- Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON M3J 1P3, Canada; Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada; Centre for Disease Modelling (CDM), York University, Toronto, ON M3J 1P3, Canada; Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, ON M3J 1P3, Canada.
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Nah K, Bede-Fazekas Á, Trájer AJ, Wu J. The potential impact of climate change on the transmission risk of tick-borne encephalitis in Hungary. BMC Infect Dis 2020; 20:34. [PMID: 31931734 PMCID: PMC6958747 DOI: 10.1186/s12879-019-4734-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 12/24/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Impact of climate change on tick-borne encephalitis (TBE) prevalence in the tick-host enzootic cycle in a given region depends on how the region-specific climate change patterns influence tick population development processes and tick-borne encephalitis virus (TBEV) transmission dynamics involving both systemic and co-feeding transmission routes. Predicting the transmission risk of TBEV in the enzootic cycle with projected climate conditions is essential for planning public health interventions including vaccination programs to mitigate the TBE incidence in the inhabitants and travelers. We have previously developed and validated a mathematical model for retroactive analysis of weather fluctuation on TBE prevalence in Hungary, and we aim to show in this research that this model provides an effective tool for projecting TBEV transmission risk in the enzootic cycle. METHODS Using the established model of TBEV transmission and the climate predictions of the Vas county in western Hungary in 2021-2050 and 2071-2100, we quantify the risk of TBEV transmission using a series of summative indices - the basic reproduction number, the duration of infestation, the stage-specific tick densities, and the accumulated (tick) infections due to co-feeding transmission. We also measure the significance of co-feeding transmission by observing the cumulative number of new transmissions through the non-systemic transmission route. RESULTS The transmission potential and the risk in the study site are expected to increase along with the increase of the temperature in 2021-2050 and 2071-2100. This increase will be facilitated by the expected extension of the tick questing season and the increase of the numbers of susceptible ticks (larval and nymphal) and the number of infected nymphal ticks co-feeding on the same hosts, leading to compounded increase of infections through the non-systemic transmission. CONCLUSIONS The developed mathematical model provides an effective tool for predicting TBE prevalence in the tick-host enzootic cycle, by integrating climate projection with emerging knowledge about the region-specific tick ecological and pathogen enzootic processes (through model parametrization fitting to historical data). Model projects increasing co-feeding transmission and prevalence of TBEV in a recognized TBE endemic region, so human risk of TBEV infection is likely increasing unless public health interventions are enhanced.
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Affiliation(s)
- Kyeongah Nah
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, 4700 Keele St., Toronto, M3J 1P3 Canada
| | - Ákos Bede-Fazekas
- MTA Centre for Ecological Research, Institute of Ecology and Botany, Alkomány u. 2-4., Vácrátót, H-2163 Hungary
- MTA Centre for Ecological Research, GINOP Sustainable Ecosystems Group, Klebelsberg Kuno u. 3., Tihany, H-8237 Hungary
| | - Attila János Trájer
- Institute of Environmental Engineering, University of Pannonia, Egyetem u. 10., Veszprém, H-8200 Hungary
- Department of Limnology, University of Pannonia, Egyetem u. 10., Veszprém, H-8200 Hungary
| | - Jianhong Wu
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, 4700 Keele St., Toronto, M3J 1P3 Canada
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9
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Wu J, Tang B, Bragazzi NL, Nah K, McCarthy Z. Quantifying the role of social distancing, personal protection and case detection in mitigating COVID-19 outbreak in Ontario, Canada. J Math Ind 2020; 10:15. [PMID: 32501416 PMCID: PMC7249976 DOI: 10.1186/s13362-020-00083-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 05/12/2020] [Indexed: 05/03/2023]
Abstract
Public health interventions have been implemented to mitigate the spread of coronavirus disease 2019 (COVID-19) in Ontario, Canada; however, the quantification of their effectiveness remains to be done and is important to determine if some of the social distancing measures can be relaxed without resulting in a second wave. We aim to equip local public health decision- and policy-makers with mathematical model-based quantification of implemented public health measures and estimation of the trend of COVID-19 in Ontario to inform future actions in terms of outbreak control and de-escalation of social distancing. Our estimates confirm that (1) social distancing measures have helped mitigate transmission by reducing daily infection contact rate, but the disease transmission probability per contact remains as high as 0.145 and case detection rate was so low that the effective reproduction number remained higher than the threshold for disease control until the closure of non-essential business in the Province; (2) improvement in case detection rate and closure of non-essential business had resulted in further reduction of the effective control number to under the threshold. We predict the number of confirmed cases according to different control efficacies including a combination of reducing further contact rates and transmission probability per contact. We show that improved case detection rate plays a decisive role to reduce the effective reproduction number, and there is still much room in terms of improving personal protection measures to compensate for the strict social distancing measures.
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Affiliation(s)
- Jianhong Wu
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Canada
- Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, Canada
| | - Biao Tang
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Canada
- Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, Canada
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Canada
- Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, Canada
| | - Kyeongah Nah
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Canada
- Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, Canada
| | - Zachary McCarthy
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Canada
- Fields-CQAM Laboratory of Mathematics for Public Health, York University, Toronto, Canada
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10
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Nah K, Magpantay FMG, Bede-Fazekas Á, Röst G, Trájer AJ, Wu X, Zhang X, Wu J. Assessing systemic and non-systemic transmission risk of tick-borne encephalitis virus in Hungary. PLoS One 2019; 14:e0217206. [PMID: 31163042 PMCID: PMC6548428 DOI: 10.1371/journal.pone.0217206] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 05/07/2019] [Indexed: 01/28/2023] Open
Abstract
Estimating the tick-borne encephalitis (TBE) infection risk under substantial uncertainties of the vector abundance, environmental condition and human-tick interaction is important for evidence-informed public health intervention strategies. Estimating this risk is computationally challenging since the data we observe, i.e., the human incidence of TBE, is only the final outcome of the tick-host transmission and tick-human contact processes. The challenge also increases since the complex TBE virus (TBEV) transmission cycle involves the non-systemic route of transmission between co-feeding ticks. Here, we describe the hidden Markov transition process, using a novel TBEV transmission-human case reporting cascade model that couples the susceptible-infected compartmental model describing the TBEV transmission dynamics among ticks, animal hosts and humans, with the stochastic observation process of human TBE reporting given infection. By fitting human incidence data in Hungary to the transmission model, we estimate key parameters relevant to the tick-host interaction and tick-human transmission. We then use the parametrized cascade model to assess the transmission potential of TBEV in the enzootic cycle with respect to the climate change, and to evaluate the contribution of non-systemic transmission. We show that the TBEV transmission potential in the enzootic cycle has been increasing along with the increased temperature though the TBE human incidence has dropped since 1990s, emphasizing the importance of persistent public health interventions. By demonstrating that non-systemic transmission pathway is a significant factor in the transmission of TBEV in Hungary, we conclude that the risk of TBE infection will be highly underestimated if the non-systemic transmission route is neglected in the risk assessment.
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Affiliation(s)
- Kyeongah Nah
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | | | - Ákos Bede-Fazekas
- Institute of Ecology and Botany, MTA Centre for Ecological Research, Vácrátót, Hungary
- GINOP Sustainable Ecosystems Group, MTA Centre for Ecological Research, Tihany, Hungary
| | - Gergely Röst
- Wolfson Centre for Mathematical Biology, University of Oxford, Oxford, United Kingdom
- Bolyai Institute, University of Szeged, Szeged, Hungary
| | - Attila János Trájer
- Department of Limnology, University of Pannonia, Veszprém, Hungary
- Institute of Environmental Engineering, University of Pannonia, Veszprém, Hungary
| | - Xiaotian Wu
- College of Arts and Sciences, Shanghai Maritime University, Shanghai, China
| | - Xue Zhang
- Department of Mathematics, Northeastern University, Shenyang, China
| | - Jianhong Wu
- Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
- * E-mail:
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Nah K, Nishiura H, Tsuchiya N, Sun X, Asai Y, Imamura A. Test-and-treat approach to HIV/AIDS: a primer for mathematical modeling. Theor Biol Med Model 2017; 14:16. [PMID: 28870213 PMCID: PMC5583977 DOI: 10.1186/s12976-017-0062-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Accepted: 08/18/2017] [Indexed: 12/13/2022] Open
Abstract
The public benefit of test-and-treat has induced a need to justify goodness for the public, and mathematical modeling studies have played a key role in designing and evaluating the test-and-treat strategy for controlling HIV/AIDS. Here we briefly and comprehensively review the essence of contemporary understanding of the test-and-treat policy through mathematical modeling approaches and identify key pitfalls that have been identified to date. While the decrease in HIV incidence is achieved with certain coverages of diagnosis, care and continued treatment, HIV prevalence is not necessarily decreased and sometimes the test-and-treat is accompanied by increased long-term cost of antiretroviral therapy (ART). To confront with the complexity of assessment on this policy, the elimination threshold or the effective reproduction number has been proposed for its use in determining the overall success to anticipate the eventual elimination. Since the publication of original model in 2009, key issues of test-and-treat modeling studies have been identified, including theoretical problems surrounding the sexual partnership network, heterogeneities in the transmission dynamics, and realistic issues of achieving and maintaining high treatment coverage in the most hard-to-reach populations. To explicitly design country-specific control policy, quantitative modeling approaches to each single setting with differing epidemiological context would require multi-disciplinary collaborations among clinicians, public health practitioners, laboratory technologists, epidemiologists and mathematical modelers.
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Affiliation(s)
- Kyeongah Nah
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo, 060-8638, Japan.,CREST, Japan Science and Technology Agency, 4-1-8, Honcho, Kawaguchi-shi, Saitama, 332-0012, Japan
| | - Hiroshi Nishiura
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo, 060-8638, Japan. .,CREST, Japan Science and Technology Agency, 4-1-8, Honcho, Kawaguchi-shi, Saitama, 332-0012, Japan.
| | - Naho Tsuchiya
- Department of Preventive Medicine and Epidemiology, Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan
| | - Xiaodan Sun
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo, 060-8638, Japan.,Department of Applied Mathematics, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Yusuke Asai
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo, 060-8638, Japan.,CREST, Japan Science and Technology Agency, 4-1-8, Honcho, Kawaguchi-shi, Saitama, 332-0012, Japan
| | - Akifumi Imamura
- Department of Infectious Diseases, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, 113-8677, Japan
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Nah K, Otsuki S, Chowell G, Nishiura H. Predicting the international spread of Middle East respiratory syndrome (MERS). BMC Infect Dis 2016; 16:356. [PMID: 27449387 PMCID: PMC4957429 DOI: 10.1186/s12879-016-1675-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Accepted: 06/10/2016] [Indexed: 11/22/2022] Open
Abstract
Background The Middle East respiratory syndrome (MERS) associated coronavirus has been imported via travelers into multiple countries around the world. In order to support risk assessment practice, the present study aimed to devise a novel statistical model to quantify the country-level risk of experiencing an importation of MERS case. Methods We analyzed the arrival time of each reported MERS importation around the world, i.e., the date on which imported cases entered a specific country, which was modeled as a dependent variable in our analysis. We also used openly accessible data including the airline transportation network to parameterize a hazard-based risk prediction model. The hazard was assumed to follow an inverse function of the effective distance (i.e., the minimum effective length of a path from origin to destination), which was calculated from the airline transportation data, from Saudi Arabia to each country. Both country-specific religion and the incidence data of MERS in Saudi Arabia were used to improve our model prediction. Results Our estimates of the risk of MERS importation appeared to be right skewed, which facilitated the visual identification of countries at highest risk of MERS importations in the right tail of the distribution. The simplest model that relied solely on the effective distance yielded the best predictive performance (Area under the curve (AUC) = 0.943) with 100 % sensitivity and 79.6 % specificity. Out of the 30 countries estimated to be at highest risk of MERS case importation, 17 countries (56.7 %) have already reported at least one importation of MERS. Although model fit measured by Akaike Information Criterion (AIC) was improved by including country-specific religion (i.e. Muslim majority country), the predictive performance as measured by AUC was not improved after accounting for this covariate. Conclusions Our relatively simple statistical model based on the effective distance derived from the airline transportation network data was found to help predicting the risk of importing MERS at the country level. The successful application of the effective distance model to predict MERS importations, particularly when computationally intensive large-scale transmission models may not be immediately applicable could have been benefited from the particularly low transmissibility of the MERS coronavirus.
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Affiliation(s)
- Kyeongah Nah
- Bolyai Institute, University of Szeged, Aradi vértanúk tere 1, Szeged, H-6720, Hungary.,Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan.,Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido, 060-8638, Japan
| | - Shiori Otsuki
- Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan.,CREST, Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi, Saitama, 332-0012, Japan
| | - Gerardo Chowell
- School of Public Health, Georgia State University, Atlanta, Georgia, USA.,Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Hiroshi Nishiura
- Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 1130033, Japan. .,CREST, Japan Science and Technology Agency, Honcho 4-1-8, Kawaguchi, Saitama, 332-0012, Japan. .,Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido, 060-8638, Japan.
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Nah K, Mizumoto K, Miyamatsu Y, Yasuda Y, Kinoshita R, Nishiura H. Estimating risks of importation and local transmission of Zika virus infection. PeerJ 2016; 4:e1904. [PMID: 27069825 PMCID: PMC4824915 DOI: 10.7717/peerj.1904] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Accepted: 03/16/2016] [Indexed: 11/20/2022] Open
Abstract
Background. An international spread of Zika virus (ZIKV) infection has attracted global attention. ZIKV is conveyed by a mosquito vector, Aedes species, which also acts as the vector species of dengue and chikungunya viruses. Methods. Arrival time of ZIKV importation (i.e., the time at which the first imported case was diagnosed) in each imported country was collected from publicly available data sources. Employing a survival analysis model in which the hazard is an inverse function of the effective distance as informed by the airline transportation network data, and using dengue and chikungunya virus transmission data, risks of importation and local transmission were estimated. Results. A total of 78 countries with imported case(s) have been identified, with the arrival time ranging from 1 to 44 weeks since the first ZIKV was identified in Brazil, 2015. Whereas the risk of importation was well explained by the airline transportation network data, the risk of local transmission appeared to be best captured by additionally accounting for the presence of dengue and chikungunya viruses. Discussion. The risk of importation may be high given continued global travel of mildly infected travelers but, considering that the public health concerns over ZIKV infection stems from microcephaly, it is more important to focus on the risk of local and widespread transmission that could involve pregnant women. The predicted risk of local transmission was frequently seen in tropical and subtropical countries with dengue or chikungunya epidemic experience.
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Affiliation(s)
- Kyeongah Nah
- Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; CREST, Japan Science and Technology Agency, Saitama, Japan; Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Kenji Mizumoto
- Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Graduate School of Medicine, Hokkaido University, Sapporo, Japan; Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Yuichiro Miyamatsu
- Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; CREST, Japan Science and Technology Agency, Saitama, Japan; Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Yohei Yasuda
- Graduate School of Medicine, The University of Tokyo , Tokyo , Japan
| | - Ryo Kinoshita
- Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; CREST, Japan Science and Technology Agency, Saitama, Japan; Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Hiroshi Nishiura
- Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; CREST, Japan Science and Technology Agency, Saitama, Japan; Graduate School of Medicine, Hokkaido University, Sapporo, Japan
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Nishiura H, Kinoshita R, Mizumoto K, Yasuda Y, Nah K. Transmission potential of Zika virus infection in the South Pacific. Int J Infect Dis 2016; 45:95-7. [PMID: 26923081 DOI: 10.1016/j.ijid.2016.02.017] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Revised: 02/22/2016] [Accepted: 02/22/2016] [Indexed: 10/22/2022] Open
Abstract
OBJECTIVES Zika virus has spread internationally through countries in the South Pacific and Americas. The present study aimed to estimate the basic reproduction number, R0, of Zika virus infection as a measurement of the transmission potential, reanalyzing past epidemic data from the South Pacific. METHODS Incidence data from two epidemics, one on Yap Island, Federal State of Micronesia in 2007 and the other in French Polynesia in 2013-2014, were reanalyzed. R0 of Zika virus infection was estimated from the early exponential growth rate of these two epidemics. RESULTS The maximum likelihood estimate (MLE) of R0 for the Yap Island epidemic was in the order of 4.3-5.8 with broad uncertainty bounds due to the small sample size of confirmed and probable cases. The MLE of R0 for French Polynesia based on syndromic data ranged from 1.8 to 2.0 with narrow uncertainty bounds. CONCLUSIONS The transmissibility of Zika virus infection appears to be comparable to those of dengue and chikungunya viruses. Considering that Aedes species are a shared vector, this finding indicates that Zika virus replication within the vector is perhaps comparable to dengue and chikungunya.
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Affiliation(s)
- Hiroshi Nishiura
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido 060-8638, Japan; CREST, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan; Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, 1130033, Japan.
| | - Ryo Kinoshita
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido 060-8638, Japan; CREST, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan; Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, 1130033, Japan
| | - Kenji Mizumoto
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido 060-8638, Japan; Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, 1130033, Japan; Graduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Tokyo, Japan
| | - Yohei Yasuda
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido 060-8638, Japan
| | - Kyeongah Nah
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido 060-8638, Japan; CREST, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan; Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, 1130033, Japan
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Kim BN, Nah K, Chu C, Ryu SU, Kang YH, Kim Y. Optimal Control Strategy of Plasmodium vivax Malaria Transmission in Korea. Osong Public Health Res Perspect 2013; 3:128-36. [PMID: 24159504 PMCID: PMC3738709 DOI: 10.1016/j.phrp.2012.07.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2012] [Revised: 07/15/2012] [Accepted: 07/20/2012] [Indexed: 11/27/2022] Open
Abstract
Objective To investigate the optimal control strategy for Plasmodium vivax malaria transmission in Korea. Methods A Plasmodium vivax malaria transmission model with optimal control terms using a deterministic system of differential equations is presented, and analyzed mathematically and numerically. Results If the cost of reducing the reproduction rate of the mosquito population is more than that of prevention measures to minimize mosquito-human contacts, the control of mosquito-human contacts needs to be taken for a longer time, comparing the other situations. More knowledge about the actual effectiveness and costs of control intervention measures would give more realistic control strategies. Conclusion Mathematical model and numerical simulations suggest that the use of mosquito-reduction strategies is more effective than personal protection in some cases but not always.
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Affiliation(s)
- Byul Nim Kim
- Department of Mathematics, Kyungpook National University, Daegu, Korea
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Nah K, Kim Y, Lee JM. The dilution effect of the domestic animal population on the transmission of P. vivax malaria. J Theor Biol 2010; 266:299-306. [PMID: 20619273 DOI: 10.1016/j.jtbi.2010.06.032] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2010] [Revised: 06/23/2010] [Accepted: 06/23/2010] [Indexed: 11/25/2022]
Abstract
The diversion of disease carrying insect from humans to animals may reduce transmission of diseases such as malaria. The use of animals to mitigate mosquito bites on human is called 'zooprophylaxis'. We introduce a mathematical model for Plasmodium vivax malaria transmission with two bloodmeal hosts (humans and domestic animals) to study the effect of zooprophylaxis. After computing the basic reproduction number from the proposed model, we explore how perturbations in the parameters, sensitive to the effects of control measures, affect its value. Zooprophylaxis is shown to determine whether a basic reproduction becomes bigger than an outbreak threshold value or not. Sensitivity analysis shows that increasing the relative animal population size works better in P. vivax malaria control than decreasing the mosquito population when the relative animal population size is larger than a threshold value.
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Affiliation(s)
- Kyeongah Nah
- Department of Mathematics, Kyungpook National University, Daegu 702-701, Republic of Korea
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Abstract
The design of workspaces and consumer products using the bivariate approach is an important, but cumbersome process because of the general unavailability of the proper bivariate charts and the calculations required. This paper presents a simplified method for bivariate anthropometric design using computer generated charts. These charts are created based on the assumption that the appropriate anthropometric data can be adequately represented by the bivariate normal distribution. Four basic bivariate design types are chosen for the study and the ability to derive other types from them is detailed. This paper presents the necessary charts and tables for quick and easy reference in order to determine cutoffs based on the simplifying assumption that the bivariate target percentage will be met by using either equal or complementary univariate cutoff values for each variable as determined by the design problem. Examples show how to use the charts and tables for different design types.
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Affiliation(s)
- K Nah
- Tufts University, Department of Engineering Design, Medford, MA 02155, USA
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