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Wang L, Wu Y, He Y, Zhang Y. Construction of effective reproduction number of infectious disease individuals based on spatiotemporal discriminant search model: take hand-foot-mouth disease as an example. BMC Med Res Methodol 2024; 24:173. [PMID: 39118030 DOI: 10.1186/s12874-024-02282-7] [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: 11/19/2023] [Accepted: 07/12/2024] [Indexed: 08/10/2024] Open
Abstract
OBJECTIVE In order to facilitate the tracing of infectious diseases in a small area and to effectively carry out disease control and epidemiological investigations, this research proposes a novel spatiotemporal model to estimate effective reproduction number(Re)for infectious diseases, based on the fundamental concept of contact tracing. METHODS This study utilizes the incidence of hand, foot, and mouth disease (HFMD) among children in Bishan District, Chongqing, China from 2015 to 2019. The study incorporates the epidemiological characteristics of HFMD and aims to construct a Spatiotemporal Correlation Discrimination of HFMD. Utilizing ARC ENGINE and C# programming for the creation of a spatio-temporal database dedicated to HFMD to facilitate data collection and analysis. The scientific validity of the proposed method was verified by comparing the effective reproduction number obtained by the traditional SEIR model. RESULTS We have ascertained the optimal search radius for the spatiotemporal search model to be 1.5 km. Upon analyzing the resulting Re values, which range from 1.14 to 4.75, we observe a skewed distribution pattern from 2015 to 2019. The median and quartile Re value recorded is 2.42 (1.98, 2.72). Except for 2018, the similarity coefficient r of the years 2015, 2016, 2017, and 2019 were all close to 1, and p <0.05 in the comparison of the two models, indicating that the Re values obtained by using the search model and the traditional SEIR model are correlated and closely related. The results exhibited similarity between the Re curves of both models and the epidemiological characteristics of HFMD. Finally, we illustrated the regional distribution of Re values obtained by the search model at various time intervals on Geographic Information System (GIS) maps which highlighted variations in the incidence of diseases across different communities, neighborhoods, and even smaller areas. CONCLUSION The model comprehensively considers both temporal variation and spatial heterogeneity in disease transmission and accounts for each individual's distinct time of onset and spatial location. This proposed method differs significantly from existing mathematical models used for estimating Re in that it is founded on reasonable scientific assumptions and computer algorithms programming that take into account real-world spatiotemporal factors. It is particularly well-suited for estimating the Re of infectious diseases in relatively stable mobile populations within small geographical areas.
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Affiliation(s)
- Linyi Wang
- Pediatric Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Yue Wu
- School of Civil and Hydraulic Engineering, Chongqing University of Science & Technology, Chongqing, China
| | - Yin He
- Pediatric Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Yu Zhang
- Bishan District Center for Disease Control, Chongqing, China
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Three Years of African Swine Fever in South Korea (2019–2021): A Scoping Review of Epidemiological Understanding. Transbound Emerg Dis 2023. [DOI: 10.1155/2023/4686980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
African swine fever (ASF) is a highly contagious viral disease in domestic pigs and wild boar that causes tremendous socioeconomic damage in related industries. In 2019, the virus emerged in South Korea, which has since reported 21 outbreaks in domestic pig farms and over 2,600 cases in wild boar. In this review, we synthesize the epidemiological knowledge generated on ASF in South Korea during the first three years of the epidemic (2019–2021). We searched four international and one domestic Korean database to identify scientific articles published since 2019 and describing ASF epidemiology in South Korea. Fourteen articles met our selection criteria and were used to synthesize the origin of ASF in South Korea, the risk factors of disease occurrence, the effectiveness of the surveillance and intervention measures that were implemented, and the viral transmission dynamics. We found that timely intensive surveillance and interventions on domestic pig farms successfully blocked between-farm transmission. However, in wild boar, the ASF virus has spread massively towards the south primarily along the mountain ranges despite ongoing fence erection and intensive depopulation efforts, endangering domestic pig farms across the country. The current devastating epidemic is suspected to be the consequence of an ASF control strategy unaligned to the epidemiological context, the challenging implementation of control measures hindered by topological complexities, and inappropriate biosecurity by field workers. To improve our understanding of ASF epidemiology in South Korea and enhance disease management, future research studies should specify the ecological drivers of disease distribution and spread and devise effective control strategies, particularly in relation to Korean topography, and the latent spread of the virus in wild boar populations. Additionally, research studies should explore the psychosocial factors for ASF management, and develop tools to support evidence-based decision-making for managing ASFV in wild boar.
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Libál A, Forgács P, Néda Á, Reichhardt C, Hengartner N, Reichhardt CJO. Transition from susceptible-infected to susceptible-infected-recovered dynamics in a susceptible-cleric-zombie-recovered active matter model. Phys Rev E 2023; 107:024604. [PMID: 36932562 DOI: 10.1103/physreve.107.024604] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 12/18/2022] [Indexed: 06/18/2023]
Abstract
The susceptible-infected (SI) and susceptible-infected-recovered (SIR) models provide two distinct representations of epidemic evolution, distinguished by whether or not the number of susceptibles always drops to zero at long times. Here we introduce a new active matter epidemic model, the "susceptible-cleric-zombie-recovered" (SCZR) model, in which spontaneous recovery is absent but zombies can recover with probability γ via interaction with a cleric. Upon colliding with a zombie, both susceptibles and clerics enter the zombie state with probability β and α, respectively. By changing the initial fraction of clerics or their healing ability rate γ, we can tune the SCZR model between SI dynamics, in which no susceptibles or clerics remain at long times, and SIR dynamics, in which a finite number of clerics and susceptibles survive at long times. The model is relevant to certain real world diseases such as HIV where spontaneous recovery is impossible but where medical interventions by a limited number of caregivers can reduce or eliminate the spread of infection.
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Affiliation(s)
- A Libál
- Mathematics and Computer Science Department, Babeş-Bolyai University, Cluj-Napoca 400084, Romania
| | - P Forgács
- Mathematics and Computer Science Department, Babeş-Bolyai University, Cluj-Napoca 400084, Romania
| | - Á Néda
- Mathematics and Computer Science Department, Babeş-Bolyai University, Cluj-Napoca 400084, Romania
| | - C Reichhardt
- Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - N Hengartner
- Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - C J O Reichhardt
- Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
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Zhang Y, Wang L, Jiang Z, Yan H, Liu X, Gu J, Wang G, Cheng X, Leng Q, Long Q, Liang Z, Wang J, Liang L, Qiu Y, Chen L, Hong H. Estimating Costs of the HIV Comprehensive Intervention Using the Spectrum Model - China, 2015-2019. China CDC Wkly 2022; 4:554-559. [PMID: 35813887 PMCID: PMC9260083 DOI: 10.46234/ccdcw2022.119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 03/10/2022] [Indexed: 12/03/2022] Open
Abstract
INTRODUCTION In order to facilitate human immunodeficiency virus (HIV) treatment and prevention, the resource needs for HIV national strategic planning in developing regions were estimated based on Spectrum, the universal HIV cost-effectiveness analysis software. METHODS Based on the theoretical framework of Spectrum, the study developed a cost measurement tool for HIV, and calculated the cost of HIV prevention and control in 6 sampled cities in China during 2015-2019 using the Spectrum model. RESULTS From 2015 to 2019, the average annual costs for HIV prevention and control for Shijiazhuang, Yantai, Ningbo, Zhenjiang, Foshan, and Wuxi cities were 46.78, 47.55, 137.49, 24.73, 74.37, and 58.30 million Chinese yuan (CNY), respectively. The per capita costs were 4.37, 6.73, 17.33, 7.77, 17.56, and 8.91 CNY, respectively. In terms of the cost structure, the ratio of preventive intervention funds to therapeutic intervention funds (antiviral treatment) varied in sampled cities. DISCUSSION Developing comprehensive and systematic HIV fund calculation methods can provide a research basis for rational resource allocation in the field of HIV.
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Affiliation(s)
- Youran Zhang
- School of Health Service Management, Anhui Medical University, Hefei City, Anhui Province, China
| | - Lili Wang
- School of Health Service Management, Anhui Medical University, Hefei City, Anhui Province, China
| | - Zhen Jiang
- Division of Prevention and intervention, National Center for AIDS and STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing Municipality, China,Jiangzhen,
| | - Hongjing Yan
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing City, Guangxi Zhuang Autonomous Region, China
| | - Xiaoxia Liu
- Zhenjiang Center for Disease Control and Prevention, Zhenjiang City, Jiangsu Province, China
| | - Jing Gu
- Wuxi Center for Disease Control and Prevention, Wuxi City, Jiangsu Province, China
| | - Guoyong Wang
- Shandong Provincial Center for Disease Control and Prevention, Jinan City, Jiangsu Province, China
| | - Xiaosong Cheng
- Yantai Center for Disease Control and Prevention, Yantai City, Shandong Province, China
| | - Qiyan Leng
- Yantai Center for Disease Control and Prevention, Yantai City, Shandong Province, China
| | - Qisui Long
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou City, Guangdong Province, China
| | - Zimian Liang
- Foshan Center for Disease Control and Prevention, Foshan City, Guangdong Province, China
| | - Jing Wang
- Foshan Center for Disease Control and Prevention, Foshan City, Guangdong Province, China
| | - Liang Liang
- Hebei Provincial Center for Disease Control and Prevention, Shijiazhuang City, Hebei Province, China
| | - Yanchao Qiu
- Shijiazhuang Center for Disease Control and Prevention, Shijiazhuang City, Hebei Province, China
| | - Lin Chen
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou City, Zhejiang Province, China
| | - Hang Hong
- Ningbo Center for Disease Control and Prevention, Ningbo City, Zhejiang Province, China
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Gunaratne C, Reyes R, Hemberg E, O'Reilly UM. Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework. Sci Rep 2022; 12:6202. [PMID: 35418652 PMCID: PMC9007058 DOI: 10.1038/s41598-022-09942-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 03/24/2022] [Indexed: 12/24/2022] Open
Abstract
Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with an agent-based SIR model to assess the relative risks of different intervention strategies. By applying our model on MIT's Stata center, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that reducing the frequency at which individuals leave their workstations combined with lowering the number of individuals admitted below the current recommendations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the agent-based SIR model, we compare relative infection risk of four respiratory illnesses, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.
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Affiliation(s)
- Chathika Gunaratne
- Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA.
- Oak Ridge National Laboratory, Oak Ridge, TN, USA.
| | - Rene Reyes
- Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
| | - Erik Hemberg
- Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
| | - Una-May O'Reilly
- Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
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Bozzani FM, Diaconu K, Gomez GB, Karat AS, Kielmann K, Grant AD, Vassall A. Using system dynamics modelling to estimate the costs of relaxing health system constraints: a case study of tuberculosis prevention and control interventions in South Africa. Health Policy Plan 2021; 37:369-375. [PMID: 34951631 PMCID: PMC8896337 DOI: 10.1093/heapol/czab155] [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: 06/02/2021] [Revised: 12/15/2021] [Accepted: 12/23/2021] [Indexed: 01/04/2023] Open
Abstract
Health system constraints are increasingly recognized as an important addition to model-based analyses of disease control interventions, as they affect achievable impact and scale. Enabling activities implemented alongside interventions to relax constraints and reach the intended coverage may incur additional costs, which should be considered in priority setting decisions. We explore the use of group model building, a participatory system dynamics modelling technique, for eliciting information from key stakeholders on the constraints that apply to tuberculosis infection prevention and control processes within primary healthcare clinics in South Africa. This information was used to design feasible interventions, including the necessary enablers to relax existing constraints. Intervention and enabler costs were then calculated at two clinics in KwaZulu-Natal using input prices and quantities from the published literature and local suppliers. Among the proposed interventions, the most inexpensive was retrofitting buildings to improve ventilation (US$1644 per year), followed by maximizing the use of community sites for medication collection among stable patients on antiretroviral therapy (ART; US$3753) and introducing appointments systems to reduce crowding (US$9302). Enablers identified included enhanced staff training, supervision and patient engagement activities to support behaviour change and local ownership. Several of the enablers identified by the stakeholders, such as obtaining building permissions or improving information flow between levels of the health systems, were not amenable to costing. Despite this limitation, an approach to costing rooted in system dynamics modelling can be successfully applied in economic evaluations to more accurately estimate the 'real world' opportunity cost of intervention options. Further empirical research applying this approach to different intervention types (e.g. new preventive technologies or diagnostics) may identify interventions that are not cost-effective in specific contexts based on the size of the required investment in enablers.
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Affiliation(s)
- Fiammetta M Bozzani
- *Corresponding author. Department of Global Health and Development, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK. E-mail:
| | - Karin Diaconu
- Institute for Global Health and Development, Queen Margaret University, Queen Margaret University Way, Musselburgh EH21 6UU, UK
| | - Gabriela B Gomez
- Department of Global Health and Development, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK
| | - Aaron S Karat
- Institute for Global Health and Development, Queen Margaret University, Queen Margaret University Way, Musselburgh EH21 6UU, UK,TB Centre, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Karina Kielmann
- Institute for Global Health and Development, Queen Margaret University, Queen Margaret University Way, Musselburgh EH21 6UU, UK
| | - Alison D Grant
- TB Centre, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK,Africa Health Research Institute, School of Laboratory Medicine & Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Nelson R. Mandela Medical School, 719 Umbilo Road, Umbilo, Durban 4001, South Africa,School of Public Health, University of the Witwatersrand, 27 Street, Andrews Road, Parktown 2193, South Africa
| | - Anna Vassall
- Department of Global Health and Development, London School of Hygiene & Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK
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