1
|
Zheng J, Shen G, Hu S, Han X, Zhu S, Liu J, He R, Zhang N, Hsieh CW, Xue H, Zhang B, Shen Y, Mao Y, Zhu B. Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review. BMC Infect Dis 2022; 22:723. [PMID: 36064333 PMCID: PMC9442567 DOI: 10.1186/s12879-022-07669-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/03/2022] [Indexed: 11/20/2022] Open
Abstract
Background The prevalence of infectious diseases remains one of the major challenges faced by the Chinese health sector. Policymakers have a tremendous interest in investigating the spatiotemporal epidemiology of infectious diseases. We aimed to review the small-scale (city level, county level, or below) spatiotemporal epidemiology of notifiable infectious diseases in China through a systematic review, thus summarizing the evidence to facilitate more effective prevention and control of the diseases. Methods We searched four English language databases (PubMed, EMBASE, Cochrane Library, and Web of Science) and three Chinese databases (CNKI, WanFang, and SinoMed), for studies published between January 1, 2004 (the year in which China’s Internet-based disease reporting system was established) and December 31, 2021. Eligible works were small-scale spatial or spatiotemporal studies focusing on at least one notifiable infectious disease, with the entire territory of mainland China as the study area. Two independent reviewers completed the review process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results A total of 18,195 articles were identified, with 71 eligible for inclusion, focusing on 22 diseases. Thirty-one studies (43.66%) were analyzed using city-level data, 34 (47.89%) were analyzed using county-level data, and six (8.45%) used community or individual data. Approximately four-fifths (80.28%) of the studies visualized incidence using rate maps. Of these, 76.06% employed various spatial clustering methods to explore the spatial variations in the burden, with Moran’s I statistic being the most common. Of the studies, 40.85% explored risk factors, in which the geographically weighted regression model was the most commonly used method. Climate, socioeconomic factors, and population density were the three most considered factors. Conclusions Small-scale spatiotemporal epidemiology has been applied in studies on notifiable infectious diseases in China, involving spatiotemporal distribution and risk factors. Health authorities should improve prevention strategies and clarify the direction of future work in the field of infectious disease research in China. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07669-9.
Collapse
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
| | - Guoquan Shen
- School of Public Administration and Policy, Renmin University of China, Beijing, China
| | - Siqi Hu
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Xinxin Han
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Siyu Zhu
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Jinlin Liu
- School of Public Policy and Administration, Northwestern Polytechnical University, Xi'an, China
| | - Rongxin He
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Ning Zhang
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.,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, UK
| | - Chih-Wei Hsieh
- Department of Public Policy, City University of Hong Kong, Hong Kong, China
| | - Hao Xue
- Freeman Spogli Institute for International Studies, Stanford University, Stanford, CA, USA
| | - Bo Zhang
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Yue Shen
- Laboratory for Urban Future, School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, China
| | - 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, 518055, Guangdong, China.
| |
Collapse
|
2
|
Wadhwa A, Thakur MK. Rapid surveillance of COVID-19 by timely detection of geographically robust, alive and emerging hotspots using Particle Swarm Optimizer. APPLIED GEOGRAPHY (SEVENOAKS, ENGLAND) 2022; 144:102719. [PMID: 35645430 PMCID: PMC9127146 DOI: 10.1016/j.apgeog.2022.102719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 05/10/2022] [Accepted: 05/12/2022] [Indexed: 06/15/2023]
Abstract
A novel virus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a pandemic called Coronavirus disease 2019 (COVID-19). According to the World Health Organization, COVID-19 was first detected in Wuhan city in December 2019 and has affected 216 countries with 9473214 confirmed cases and 484249 deaths globally as on June 26th, 2020. Also, this outbreak continues to grow in many countries like the United States of America (U.S.), Brazil, India, and Russia. To ensure rapid surveillance and better decision-making by government authorities in different countries, it is vital to identify alive and emerging hotspots within a country promptly. State-of-the-art methods based on space-time scan statistics (like SaTScan) are not geographically robust. Also, due to the enumeration of many Spatio-temporal cylinders, the computation cost of Spatio-temporal SaTScan (ST-SaTScan) is very high. In the applications like COVID-19 where we need to detect the emerging hotspots daily as soon as the new count of cases gets updated, ST-SaTScan seems inefficient. Therefore, this paper proposes a Particle Swarm Optimizer-based scheme to timely detect geographically robust, alive, and emerging COVID-19 hotspots in a country. Timely detection can help government officials design better control strategies like increasing testing in hotspots, imposing stricter containment rules, or setting up temporary hospital beds. Performance of ST-SaTScan and proposed scheme have been analyzed for four worst-hit U.S. states for the incubation period of 14 days between June 11th, 2020, and June 24th, 2020. Results indicate that the proposed scheme detects hotspots of a higher likelihood ratio (a measure to indicate the significance of hotspot) than ST-SaTScan in significantly less time. We also applied the proposed scheme to detect the emerging COVID-19 hotspots in all states of the U.S. During the study period, the proposed scheme has detected 104 emerging COVID-19 hotspots.
Collapse
Affiliation(s)
- Ankita Wadhwa
- Department of Computer Science Engineering and IT, Jaypee Institute of Information Technology, A-10 Sector 62, Noida, UP, 201309, India
| | - Manish Kumar Thakur
- Department of Computer Science Engineering and IT, Jaypee Institute of Information Technology, A-10 Sector 62, Noida, UP, 201309, India
| |
Collapse
|
3
|
Keating P, Murray J, Schenkel K, Merson L, Seale A. Electronic data collection, management and analysis tools used for outbreak response in low- and middle-income countries: a systematic review and stakeholder survey. BMC Public Health 2021; 21:1741. [PMID: 34560871 PMCID: PMC8464108 DOI: 10.1186/s12889-021-11790-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 08/29/2021] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Use of electronic data collection, management and analysis tools to support outbreak response is limited, especially in low income countries. This can hamper timely decision-making during outbreak response. Identifying available tools and assessing their functions in the context of outbreak response would support appropriate selection and use, and likely more timely data-driven decision-making during outbreaks. METHODS We conducted a systematic review and a stakeholder survey of the Global Outbreak Alert and Response Network and other partners to identify and describe the use of, and technical characteristics of, electronic data tools used for outbreak response in low- and middle-income countries. Databases included were MEDLINE, EMBASE, Global Health, Web of Science and CINAHL with publications related to tools for outbreak response included from January 2010-May 2020. Software tool websites of identified tools were also reviewed. Inclusion and exclusion criteria were applied and counts, and proportions of data obtained from the review or stakeholder survey were calculated. RESULTS We identified 75 electronic tools including for data collection (33/75), management (13/75) and analysis (49/75) based on data from the review and survey. Twenty-eight tools integrated all three functionalities upon collection of additional information from the tool developer websites. The majority were open source, capable of offline data collection and data visualisation. EpiInfo, KoBoCollect and Open Data Kit had the broadest use, including for health promotion, infection prevention and control, and surveillance data capture. Survey participants highlighted harmonisation of data tools as a key challenge in outbreaks and the need for preparedness through training front-line responders on data tools. In partnership with the Global Health Network, we created an online interactive decision-making tool using data derived from the survey and review. CONCLUSIONS Many electronic tools are available for data -collection, -management and -analysis in outbreak response, but appropriate tool selection depends on knowledge of tools' functionalities and capabilities. The online decision-making tool created to assist selection of the most appropriate tool(s) for outbreak response helps by matching requirements with functionality. Applying the tool together with harmonisation of data formats, and training of front-line responders outside of epidemic periods can support more timely data-driven decision making in outbreaks.
Collapse
Affiliation(s)
- Patrick Keating
- London School of Hygiene and Tropical Medicine, London, UK. .,United Kingdom Public Health Rapid Support Team, London, UK.
| | - Jillian Murray
- London School of Hygiene and Tropical Medicine, London, UK
| | | | | | - Anna Seale
- London School of Hygiene and Tropical Medicine, London, UK.,United Kingdom Public Health Rapid Support Team, London, UK
| |
Collapse
|
4
|
Li X, Chen D, Zhang Y, Xue X, Zhang S, Chen M, Liu X, Ding G. Analysis of spatial-temporal distribution of notifiable respiratory infectious diseases in Shandong Province, China during 2005-2014. BMC Public Health 2021; 21:1597. [PMID: 34461855 PMCID: PMC8403828 DOI: 10.1186/s12889-021-11627-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 08/12/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Little comprehensive information on overall epidemic trend of notifiable respiratory infectious diseases is available in Shandong Province, China. This study aimed to determine the spatiotemporal distribution and epidemic characteristics of notifiable respiratory infectious diseases. METHODS Time series was firstly performed to describe the temporal distribution feature of notifiable respiratory infectious diseases during 2005-2014 in Shandong Province. GIS Natural Breaks (Jenks) was applied to divide the average annual incidence of notifiable respiratory infectious diseases into five grades. Spatial empirical Bayesian smoothed risk maps and excess risk maps were further used to investigate spatial patterns of notifiable respiratory infectious diseases. Global and local Moran's I statistics were used to measure the spatial autocorrelation. Spatial-temporal scanning was used to detect spatiotemporal clusters and identify high-risk locations. RESULTS A total of 537,506 cases of notifiable respiratory infectious diseases were reported in Shandong Province during 2005-2014. The morbidity of notifiable respiratory infectious diseases had obvious seasonality with high morbidity in winter and spring. Local Moran's I analysis showed that there were 5, 23, 24, 4, 20, 8, 14, 10 and 7 high-risk counties determined for influenza A (H1N1), measles, tuberculosis, meningococcal meningitis, pertussis, scarlet fever, influenza, mumps and rubella, respectively. The spatial-temporal clustering analysis determined that the most likely cluster of influenza A (H1N1), measles, tuberculosis, meningococcal meningitis, pertussis, scarlet fever, influenza, mumps and rubella included 74, 66, 58, 56, 22, 64, 2, 75 and 56 counties, and the time frame was November 2009, March 2008, January 2007, February 2005, July 2007, December 2011, November 2009, June 2012 and May 2005, respectively. CONCLUSIONS There were obvious spatiotemporal clusters of notifiable respiratory infectious diseases in Shandong during 2005-2014. More attention should be paid to the epidemiological and spatiotemporal characteristics of notifiable respiratory infectious diseases to establish new strategies for its control.
Collapse
Affiliation(s)
- Xiaomei Li
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China
| | - Dongzhen Chen
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China.,Liaocheng Center for Disease Control and Prevention, Liaocheng, 252100, Shandong Province, China
| | - Yan Zhang
- Guiqian International General Hospital, Guiyang, 550018, Guizhou Province, China
| | - Xiaojia Xue
- Qingdao Municipal Center for Disease Control & Prevention, Qingdao, 266033, Shandong Province, China
| | - Shengyang Zhang
- Shandong Center for Disease control and Prevention, Jinan, 250014, Shandong Province, China
| | - Meng Chen
- Jining Center for Disease Control and Prevention, Qingdao, 272113, Shandong Province, China
| | - Xuena Liu
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China.
| | - Guoyong Ding
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China.
| |
Collapse
|
5
|
Jiang L, Zhao X, Xu W, Zhou X, Luo C, Zhou J, Fu X, Chen Y, Li D. Emergence of human avian influenza A(H7N9) virus infections in Wenshan City in Southwest China, 2017. BMC Infect Dis 2020; 20:154. [PMID: 32075579 PMCID: PMC7031964 DOI: 10.1186/s12879-020-4858-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 02/06/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The emergence of human infection with avian influenza A(H7N9) virus was reported in Wenshan City, southwestern China in 2017. The study describes the epidemiological and virological features of the outbreak and discusses the origin of the infection. METHODS Poultry exposure and timelines of key events for each patient were collected. Samples derived from the patients, their close contacts, and environments were tested for influenza A(H7N9) virus by real-time reverse transcription polymerase chain reaction. Genetic sequencing and phylogenetic analysis were also conducted. RESULTS Five patients were reported in the outbreak. An epidemiological investigation showed that all patients had been exposed at live poultry markets. The A(H7N9) isolates from these patients had low pathogenicity in avian species. Both epidemiological investigations of chicken sources and phylogenetic analysis of viral gene sequences indicated that the source of infection was from Guangxi Province, which lies 100 km to the east of Wenshan City. CONCLUSIONS In the study, a sudden emergence of human cases of H7N9 was documented in urban area of Wenshan City. Chickens were an important carrier in the H7N9 virus spreading from Guangxi to Wenshan. Hygienic management of live poultry markets and virological screening of chickens transported across regions should be reinforced to limit the spread of H7N9 virus.
Collapse
Affiliation(s)
- Li Jiang
- The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
| | - Xiaonan Zhao
- Yunnan Provincial Center for Disease Control and Prevention, 158 Dongsi street, Kunming, Yunnan, 650022, People's Republic of China
| | - Wen Xu
- Yunnan Provincial Center for Disease Control and Prevention, 158 Dongsi street, Kunming, Yunnan, 650022, People's Republic of China
| | - Xuehua Zhou
- Wenshan Prefecture Center for Disease Control and Prevention, Wenshan, Yunnan, China
| | - Chunrui Luo
- Yunnan Provincial Center for Disease Control and Prevention, 158 Dongsi street, Kunming, Yunnan, 650022, People's Republic of China
| | - Jiunan Zhou
- Yunnan Provincial Center for Disease Control and Prevention, 158 Dongsi street, Kunming, Yunnan, 650022, People's Republic of China
| | - Xiaoqing Fu
- Yunnan Provincial Center for Disease Control and Prevention, 158 Dongsi street, Kunming, Yunnan, 650022, People's Republic of China
| | - Yaoyao Chen
- Yunnan Provincial Center for Disease Control and Prevention, 158 Dongsi street, Kunming, Yunnan, 650022, People's Republic of China
| | - Duo Li
- Yunnan Provincial Center for Disease Control and Prevention, 158 Dongsi street, Kunming, Yunnan, 650022, People's Republic of China.
| |
Collapse
|
6
|
Guevara C, Penas MS. Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:201925-201936. [PMID: 34812367 PMCID: PMC8545257 DOI: 10.1109/access.2020.3036347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 11/03/2020] [Indexed: 05/07/2023]
Abstract
In this study, the Intelligent Infectious Diseases Algorithm (IIDA) has been developed to locate the sources of infection and survival rate of coronavirus disease 2019 (COVID-19), in order to propose health care routes for population affected by COVID-19. The main goal of this computational algorithm is to reduce the spread of the virus and decrease the number of infected people. To do so, health care routes are generated according to the priority of certain population groups. The algorithm was applied to New York state data. Based on infection rates and reported deaths, hot spots were determined by applying the kernel density estimation (KDE) to the groups that have been previously obtained using a clustering algorithm together with the elbow method. For each cluster, the survival rate -the key information to prioritize medical care- was determined using the proportional hazards model. Finally, ant colony optimization (ACO) and the traveling salesman problem (TSP) optimization algorithms were applied to identify the optimal route to the closest hospital. The results obtained efficiently covered the points with the highest concentration of COVID-19 cases. In this way, its spread can be prevented and health resources optimized.
Collapse
Affiliation(s)
- Cesar Guevara
- Centre of Mechatronics and Interactive Systems (MIST)Universidad Tecnológica Indoamérica Quito 170301 Ecuador
| | - Matilde Santos Penas
- Institute of Knowledge Technology, Complutense University of Madrid 28040 Madrid Spain
| |
Collapse
|