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Khan MI, Qureshi H, Bae SJ, Shah A, Ahmad N, Ahmad S, Asim M. Dynamics of Malaria Incidence in Khyber Pakhtunkhwa, Pakistan: Unveiling Rapid Growth Patterns and Forecasting Future Trends. J Epidemiol Glob Health 2024; 14:234-242. [PMID: 38353917 PMCID: PMC11043263 DOI: 10.1007/s44197-024-00189-6] [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: 11/05/2023] [Accepted: 01/07/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Malaria remains a formidable worldwide health challenge, with approximately half of the global population at high risk of catching the infection. This research study aimed to address the pressing public health issue of malaria's escalating prevalence in Khyber Pakhtunkhwa (KP) province, Pakistan, and endeavors to estimate the trend for the future growth of the infection. METHODS The data were collected from the IDSRS of KP, covering a period of 5 years from 2018 to 2022. We proposed a hybrid model that integrated Prophet and TBATS methods, allowing us to efficiently capture the complications of the malaria data and improve forecasting accuracy. To ensure an inclusive assessment, we compared the prediction performance of the proposed hybrid model with other widely used time series models, such as ARIMA, ETS, and ANN. The models were developed through R-statistical software (version 4.2.2). RESULTS For the prediction of malaria incidence, the suggested hybrid model (Prophet and TBATS) surpassed commonly used time series approaches (ARIMA, ETS, and ANN). Hybrid model assessment metrics portrayed higher accuracy and reliability with lower MAE (8913.9), RMSE (3850.2), and MAPE (0.301) values. According to our forecasts, malaria infections were predicted to spread around 99,301 by December 2023. CONCLUSIONS We found the hybrid model (Prophet and TBATS) outperformed common time series approaches for forecasting malaria. By December 2023, KP's malaria incidence is expected to be around 99,301, making future incidence forecasts important. Policymakers will be able to use these findings to curb disease and implement efficient policies for malaria control.
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Affiliation(s)
- Muhammad Imran Khan
- Department of Industrial Engineering, Hanyang University, Seoul, South Korea
| | - Humera Qureshi
- Department of Industrial Engineering, Hanyang University, Seoul, South Korea.
| | - Suk Joo Bae
- Department of Industrial Engineering, Hanyang University, Seoul, South Korea.
| | - Adil Shah
- Health Department, Peshawar, Khyber Pakhtunkhwa, Pakistan
| | - Naveed Ahmad
- EIAS: Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia
| | - Sadique Ahmad
- EIAS: Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia
| | - Muhammad Asim
- EIAS: Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia
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Fornace KM, Topazian HM, Routledge I, Asyraf S, Jelip J, Lindblade KA, Jeffree MS, Ruiz Cuenca P, Bhatt S, Ahmed K, Ghani AC, Drakeley C. No evidence of sustained nonzoonotic Plasmodium knowlesi transmission in Malaysia from modelling malaria case data. Nat Commun 2023; 14:2945. [PMID: 37263994 PMCID: PMC10235043 DOI: 10.1038/s41467-023-38476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 05/02/2023] [Indexed: 06/03/2023] Open
Abstract
Reported incidence of the zoonotic malaria Plasmodium knowlesi has markedly increased across Southeast Asia and threatens malaria elimination. Nonzoonotic transmission of P. knowlesi has been experimentally demonstrated, but it remains unknown whether nonzoonotic transmission is contributing to increases in P. knowlesi cases. Here, we adapt model-based inference methods to estimate RC, individual case reproductive numbers, for P. knowlesi, P. falciparum and P. vivax human cases in Malaysia from 2012-2020 (n = 32,635). Best fitting models for P. knowlesi showed subcritical transmission (RC < 1) consistent with a large reservoir of unobserved infection sources, indicating P. knowlesi remains a primarily zoonotic infection. In contrast, sustained transmission (RC > 1) was estimated historically for P. falciparum and P. vivax, with declines in RC estimates observed over time consistent with local elimination. Together, this suggests sustained nonzoonotic P. knowlesi transmission is highly unlikely and that new approaches are urgently needed to control spillover risks.
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Affiliation(s)
- Kimberly M Fornace
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
- Saw Swee Hock School of Public Health, National University of, Singapore, Singapore.
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
| | - Hillary M Topazian
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Isobel Routledge
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
- University of California, San Francisco, San Francisco, USA
| | - Syafie Asyraf
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
| | - Jenarun Jelip
- Vector-borne Disease Control Division, Ministry of Health Malaysia, Putrajaya, Malaysia
| | - Kim A Lindblade
- Global Malaria Programme, World Health Organization, Geneva, Switzerland
| | | | - Pablo Ruiz Cuenca
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
- Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Kamruddin Ahmed
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Chris Drakeley
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
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Huang F, Feng XY, Zhou SS, Tang LH, Xia ZG. Establishing and applying an adaptive strategy and approach to eliminating malaria: practice and lessons learnt from China from 2011 to 2020. Emerg Microbes Infect 2022; 11:314-325. [PMID: 34989665 PMCID: PMC8786258 DOI: 10.1080/22221751.2022.2026740] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 01/05/2022] [Indexed: 12/17/2022]
Abstract
ABSTRACTOn 30 June 2021, China was certified malaria-free by the World Health Organization. In this study, the evolution, performance, outcomes, and impact of China's adaptive strategy and approach for malaria elimination from 2011 to 2020 were analysed using 10-year data. The strategy and approach focused on timely detection and rapid responses to individual cases and foci. Indigenous cases declined from 1,308 in 2011 to 36 in 2015, and the last one was reported from Yunnan Province in April 2016, although thousands of imported cases still occur annually. The "1-3-7" approach was implemented successfully between 2013 and 2020, with 100% of cases reported within 24 h, 94.5% of cases investigated within three days of case reporting, and 93.4% of foci responses performed within seven days. Additionally, 81.6% of patients attended the first healthcare visit within 1-3 days of onset and 58.4% were diagnosed as malaria within three days of onset, in 2017-2020. The adaptive strategy and approach, along with their universal implementation, are most critical in malaria elimination. In addition to strengthening surveillance on drug resistance and vectors and border malaria collaboration, a further adapted three-step strategy and the corresponding "3-3-7" model are recommended to address the risks of re-transmission and death by imported cases after elimination. China's successful practice and lessons learnt through long-term efforts provide a reference for countries moving towards elimination.
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Affiliation(s)
- Fang Huang
- Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, National Institute of Parasitic Diseases, Shanghai, People’s Republic of China
| | - Xin-Yu Feng
- Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, National Institute of Parasitic Diseases, Shanghai, People’s Republic of China
| | - Shui-Sen Zhou
- Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, National Institute of Parasitic Diseases, Shanghai, People’s Republic of China
| | - Lin-Hua Tang
- Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, National Institute of Parasitic Diseases, Shanghai, People’s Republic of China
| | - Zhi-Gui Xia
- Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Center for Tropical Diseases, National Center for International Research on Tropical Diseases, National Institute of Parasitic Diseases, Shanghai, People’s Republic of China
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4
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Zheng J, Zhang N, Shen G, Liang F, Zhao Y, He X, Wang Y, He R, Chen W, Xue H, Shen Y, Fu Y, Zhang WH, Zhang L, Bhatt S, Mao Y, Zhu B. Spatiotemporal and Seasonal Trends of Class A and B Notifiable Infectious Diseases in China: A Retrospective Analysis (Preprint). JMIR Public Health Surveill 2022; 9:e42820. [PMID: 37103994 PMCID: PMC10176137 DOI: 10.2196/42820] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND China is the most populous country globally and has made significant achievements in the control of infectious diseases over the last decades. The 2003 SARS epidemic triggered the initiation of the China Information System for Disease Control and Prevention (CISDCP). Since then, numerous studies have investigated the epidemiological features and trends of individual infectious diseases in China; however, few considered the changing spatiotemporal trends and seasonality of these infectious diseases over time. OBJECTIVE This study aims to systematically review the spatiotemporal trends and seasonal characteristics of class A and class B notifiable infectious diseases in China during 2005-2020. METHODS We extracted the incidence and mortality data of 8 types (27 diseases) of notifiable infectious diseases from the CISDCP. We used the Mann-Kendall and Sen's methods to investigate the diseases' temporal trends, Moran I statistic for their geographical distribution, and circular distribution analysis for their seasonality. RESULTS Between January 2005 and December 2020, 51,028,733 incident cases and 261,851 attributable deaths were recorded. Pertussis (P=.03), dengue fever (P=.01), brucellosis (P=.001), scarlet fever (P=.02), AIDS (P<.001), syphilis (P<.001), hepatitis C (P<.001) and hepatitis E (P=.04) exhibited significant upward trends. Furthermore, measles (P<.001), bacillary and amebic dysentery (P<.001), malaria (P=.04), dengue fever (P=.006), brucellosis (P=.03), and tuberculosis (P=.003) exhibited significant seasonal patterns. We observed marked disease burden-related geographic disparities and heterogeneities. Notably, high-risk areas for various infectious diseases have remained relatively unchanged since 2005. In particular, hemorrhagic fever and brucellosis were largely concentrated in Northeast China; neonatal tetanus, typhoid and paratyphoid, Japanese encephalitis, leptospirosis, and AIDS in Southwest China; BAD in North China; schistosomiasis in Central China; anthrax, tuberculosis, and hepatitis A in Northwest China; rabies in South China; and gonorrhea in East China. However, the geographical distribution of syphilis, scarlet fever, and hepatitis E drifted from coastal to inland provinces during 2005-2020. CONCLUSIONS The overall infectious disease burden in China is declining; however, hepatitis C and E, bacterial infections, and sexually transmitted infections continue to multiply, many of which have spread from coastal to inland provinces.
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Affiliation(s)
- Junyao Zheng
- China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai, China
- School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China
| | - Ning Zhang
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Guoquan Shen
- School of Public Administration and Policy, Renmin University of China, Beijing, China
| | - Fengchao Liang
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China
| | - Yang Zhao
- The George Institute for Global Health, Peking University Health Science Center, Beijing, China
- WHO Collaborating Centre on Implementation Research for Prevention and Control of Noncommunicable Diseases, Melbourne, Australia
| | - Xiaochen He
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Ying Wang
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Rongxin He
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Wenna Chen
- Center for Chinese Public Administration Research and School of Government, Sun Yat-sen University, Guangzhou, China
| | - Hao Xue
- Stanford Center on China's Economy and Institutions, Stanford University, Stanford, CA, United States
| | - Yue Shen
- Laboratory for Urban Future, School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Yang Fu
- Department of public administration, School of Government, Shenzhen University, Shenzhen, China
| | - Wei-Hong Zhang
- International Centre for Reproductive Health, Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Lei Zhang
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, Australia
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College, London, United Kingdom
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Ying Mao
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Bin Zhu
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China
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Huber JH, Hsiang MS, Dlamini N, Murphy M, Vilakati S, Nhlabathi N, Lerch A, Nielsen R, Ntshalintshali N, Greenhouse B, Perkins TA. Inferring person-to-person networks of Plasmodium falciparum transmission: are analyses of routine surveillance data up to the task? Malar J 2022; 21:58. [PMID: 35189905 PMCID: PMC8860266 DOI: 10.1186/s12936-022-04072-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 01/31/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Inference of person-to-person transmission networks using surveillance data is increasingly used to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated. METHODS The influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum malaria were evaluated. RESULTS The information content of these data types may be limited for inferring person-to-person transmission networks and may lead to an overestimate of transmission. Only when outbreaks were temporally focal or travel histories were accurate was the algorithm able to accurately estimate the reproduction number under control, Rc. Applying this approach to data from Eswatini indicated that inferences of Rc and spatiotemporal patterns therein depend upon the choice of data types and assumptions about travel-history data. CONCLUSIONS These results suggest that transmission network inferences made with routine malaria surveillance data should be interpreted with caution.
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Affiliation(s)
- John H Huber
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.
| | - Michelle S Hsiang
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Malaria Elimination Initiative, Global Health Group, University of California, San Francisco, CA, USA.,Department of Pediatrics, University of California, San Francisco,, CA, USA
| | - Nomcebo Dlamini
- National Malaria Elimination Programme, Ministry of Health, Manzini, Eswatini
| | - Maxwell Murphy
- Department of Medicine, University of California, San Francisco, CA, USA
| | | | - Nomcebo Nhlabathi
- National Malaria Elimination Programme, Ministry of Health, Manzini, Eswatini
| | - Anita Lerch
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - Rasmus Nielsen
- Department of Integrative Biology and Statistics, University of California, Berkeley, CA, USA
| | | | - Bryan Greenhouse
- Department of Medicine, University of California, San Francisco, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - T Alex Perkins
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.
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Routledge I, Unwin HJT, Bhatt S. Inference of malaria reproduction numbers in three elimination settings by combining temporal data and distance metrics. Sci Rep 2021; 11:14495. [PMID: 34262054 PMCID: PMC8280212 DOI: 10.1038/s41598-021-93238-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 06/11/2021] [Indexed: 11/10/2022] Open
Abstract
Individual-level geographic information about malaria cases, such as the GPS coordinates of residence or health facility, is often collected as part of surveillance in near-elimination settings, but could be more effectively utilised to infer transmission dynamics, in conjunction with additional information such as symptom onset time and genetic distance. However, in the absence of data about the flow of parasites between populations, the spatial scale of malaria transmission is often not clear. As a result, it is important to understand the impact of varying assumptions about the spatial scale of transmission on key metrics of malaria transmission, such as reproduction numbers. We developed a method which allows the flexible integration of distance metrics (such as Euclidian distance, genetic distance or accessibility matrices) with temporal information into a single inference framework to infer malaria reproduction numbers. Twelve scenarios were defined, representing different assumptions about the likelihood of transmission occurring over different geographic distances and likelihood of missing infections (as well as high and low amounts of uncertainty in this estimate). These scenarios were applied to four individual level datasets from malaria eliminating contexts to estimate individual reproduction numbers and how they varied over space and time. Model comparison suggested that including spatial information improved models as measured by second order AIC (ΔAICc), compared to time only results. Across scenarios and across datasets, including spatial information tended to increase the seasonality of temporal patterns in reproduction numbers and reduced noise in the temporal distribution of reproduction numbers. The best performing parameterisations assumed long-range transmission (> 200 km) was possible. Our approach is flexible and provides the potential to incorporate other sources of information which can be converted into distance or adjacency matrices such as travel times or molecular markers.
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A retrospective analysis of malaria epidemiological characteristics in Yingjiang County on the China-Myanmar border. Sci Rep 2021; 11:14129. [PMID: 34239003 PMCID: PMC8266812 DOI: 10.1038/s41598-021-93734-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 06/15/2021] [Indexed: 11/08/2022] Open
Abstract
Yingjiang County, which is on the China–Myanmar border, is the main focus for malaria elimination in China. The epidemiological characteristics of malaria in Yingjiang County were analysed in a retrospective analysis. A total of 895 malaria cases were reported in Yingjiang County between 2013 and 2019. The majority of cases occurred in males (70.7%) and individuals aged 19–59 years (77.3%). Plasmodium vivax was the predominant species (96.6%). The number of indigenous cases decreased gradually and since 2017, no indigenous cases have been reported. Malaria cases were mainly distributed in the southern and southwestern areas of the county; 55.6% of the indigenous cases were reported in Nabang Township, which also had the highest risk of imported malaria. The “1–3–7” approach has been implemented effectively, with 100% of cases reported within 24 h, 88.9% cases investigated and confirmed within 3 days and 98.5% of foci responded to within 7 days. Although malaria elimination has been achieved in Yingjiang County, sustaining elimination and preventing the re-establishment of malaria require the continued strengthening of case detection, surveillance and response systems targeting the migrant population in border areas.
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Lee SA, Jarvis CI, Edmunds WJ, Economou T, Lowe R. Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions. J R Soc Interface 2021; 18:20210096. [PMID: 34034534 PMCID: PMC8150046 DOI: 10.1098/rsif.2021.0096] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/26/2021] [Indexed: 12/14/2022] Open
Abstract
Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.
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Affiliation(s)
- Sophie A. Lee
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - W. John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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9
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Unwin HJT, Routledge I, Flaxman S, Rizoiu MA, Lai S, Cohen J, Weiss DJ, Mishra S, Bhatt S. Using Hawkes Processes to model imported and local malaria cases in near-elimination settings. PLoS Comput Biol 2021; 17:e1008830. [PMID: 33793564 PMCID: PMC8043404 DOI: 10.1371/journal.pcbi.1008830] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 04/13/2021] [Accepted: 02/23/2021] [Indexed: 01/29/2023] Open
Abstract
Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.
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Affiliation(s)
- H. Juliette T. Unwin
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College, London, United Kingdom
| | - Isobel Routledge
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College, London, United Kingdom
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Seth Flaxman
- Department of Mathematics, Imperial College, London, United Kingdom
| | | | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - Justin Cohen
- Clinton Health Access Initiative, Boston, Massachusetts, United States of America
| | - Daniel J. Weiss
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Telethon Kids Institute, Perth Children’s Hospital, Nedlands, Western Australia, Australia
- Curtin University, Bentley, Western Australia, Australia
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College, London, United Kingdom
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College, London, United Kingdom
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