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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Health".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 15684

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Dr. Kinley Wangdi

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Department of Global Health, National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT 2601, Australia
Interests: infectious diseases; spatial; modelling; mapping; Bayesian; vector-borne diseases; health systems
Special Issues, Collections and Topics in MDPI journals

Dr. Nasser Bagheri

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Guest Editor

Centre for Mental Health Research, Research School of Population Health, College of Health and Medicine, Australian National University, 63 Eggleston Rd., Acton, Canberra, ACT 2601, Australia
Interests: spatial analysis in chronic diseases; contextual analysis in healthcare ecosystems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bacterial, viral, and parasitic infectious diseases continue to have a significant impact on global health, particularly emerging infectious diseases such as the coronavirus disease of 2019 (COVID-19), Ebola, dengue, chikungunya, and Zika. The etiology and epidemiology of these diseases are complex and many demographic, environmental, and social factors may play a role in the incidence of such diseases. Of these factors, global travel and trade, unplanned urbanization, and climate change have an important impact on the distribution and incidence of emerging infectious diseases. These could facilitate the transmission of pathogens from one region to another region, make transmission seasons longer or more intense, or cause diseases to emerge in countries where they were previously unknown.

Spatial analytical modeling allows for a better understanding these contextual factors and can help policy-makers to develop tailored interventions. Advanced spatial analysis and modeling can help policy-makers to identify high-risk (hot spot) areas and design geographically targeted measures for better resource allocation and disease control. This Special Issue, entitled “Spatial Epidemiology of Emerging Infectious Diseases”, welcomes high-quality original research articles and reviews on any emerging infectious diseases of public health importance.

Dr. Kinley Wangdi
Dr. Nasser Bagheri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Emerging
Infectious
Diseases
Spatial modeling
Epidemiology
Ecological
Public health

Published Papers (4 papers)

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Research

15 pages, 1775 KiB

Open AccessArticle

A Bayesian Spatio-Temporal Analysis of Malaria in the Greater Accra Region of Ghana from 2015 to 2019

by Elorm Donkor, Matthew Kelly, Cecilia Eliason, Charles Amotoh, Darren J. Gray, Archie C. A. Clements and Kinley Wangdi

Int. J. Environ. Res. Public Health 2021, 18(11), 6080; https://doi.org/10.3390/ijerph18116080 - 04 Jun 2021

Cited by 8 | Viewed by 3114

Abstract

The Greater Accra Region is the smallest of the 16 administrative regions in Ghana. It is highly populated and characterized by tropical climatic conditions. Although efforts towards malaria control in Ghana have had positive impacts, malaria remains in the top five diseases reported at healthcare facilities within the Greater Accra Region. To further accelerate progress, analysis of regionally generated data is needed to inform control and management measures at this level. This study aimed to examine the climatic drivers of malaria transmission in the Greater Accra Region and identify inter-district variation in malaria burden. Monthly malaria cases for the Greater Accra Region were obtained from the Ghanaian District Health Information and Management System. Malaria cases were decomposed using seasonal-trend decomposition, based on locally weighted regression to analyze seasonality. A negative binomial regression model with a conditional autoregressive prior structure was used to quantify associations between climatic variables and malaria risk and spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. A total of 1,105,370 malaria cases were recorded in the region from 2015 to 2019. The overall malaria incidence for the region was approximately 47 per 1000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern. Monthly malaria case incidence was found to decrease by 2.3% (95% credible interval: 0.7–4.2%) for each 1 °C increase in monthly minimum temperature. Only five districts located in the south-central part of the region had a malaria incidence rate lower than the regional average at >95% probability level. The distribution of malaria cases was heterogeneous, seasonal, and significantly associated with climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate time points could result in a significant reduction in malaria transmission in the Greater Accra Region. Full article

(This article belongs to the Special Issue Spatial Epidemiology of Emerging Infectious Diseases)

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12 pages, 2716 KiB

Open AccessArticle

Space–Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination

by Kinley Wangdi, Kinley Penjor, Tobgyal, Saranath Lawpoolsri, Ric N. Price, Peter W. Gething, Darren J. Gray, Elivelton Da Silva Fonseca and Archie C. A. Clements

Int. J. Environ. Res. Public Health 2021, 18(11), 5553; https://doi.org/10.3390/ijerph18115553 - 22 May 2021

Cited by 3 | Viewed by 2436

Abstract

Malaria in Bhutan has fallen significantly over the last decade. As Bhutan attempts to eliminate malaria in 2022, this study aimed to characterize the space–time clustering of malaria from 2010 to 2019. Malaria data were obtained from the Bhutan Vector-Borne Disease Control Program data repository. Spatial and space–time cluster analyses of Plasmodium falciparum and Plasmodium vivax cases were conducted at the sub-district level from 2010 to 2019 using Kulldorff’s space–time scan statistic. A total of 768 confirmed malaria cases, including 454 (59%) P. vivax cases, were reported in Bhutan during the study period. Significant temporal clusters of cases caused by both species were identified between April and September. The most likely spatial clusters were detected in the central part of Bhutan throughout the study period. The most likely space–time cluster was in Sarpang District and neighboring districts between January 2010 to June 2012 for cases of infection with both species. The most likely cluster for P. falciparum infection had a radius of 50.4 km and included 26 sub-districts with a relative risk (RR) of 32.7. The most likely cluster for P. vivax infection had a radius of 33.6 km with 11 sub-districts and RR of 27.7. Three secondary space–time clusters were detected in other parts of Bhutan. Spatial and space–time cluster analysis identified high-risk areas and periods for both P. vivax and P. falciparum malaria. Both malaria types showed significant spatial and spatiotemporal variations. Operational research to understand the drivers of residual transmission in hotspot sub-districts will help to overcome the final challenges of malaria elimination in Bhutan. Full article

(This article belongs to the Special Issue Spatial Epidemiology of Emerging Infectious Diseases)

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13 pages, 2594 KiB

Open AccessArticle

Spatial Analysis of HIV Infection and Associated Risk Factors in Botswana

by Malebogo Solomon, Luis Furuya-Kanamori and Kinley Wangdi

Int. J. Environ. Res. Public Health 2021, 18(7), 3424; https://doi.org/10.3390/ijerph18073424 - 25 Mar 2021

Cited by 5 | Viewed by 4578

Abstract

Botswana has the third highest human immunodeficiency virus (HIV) prevalence globally, and the severity of the epidemic within the country varies considerably between the districts. This study aimed to identify clusters of HIV and associated factors among adults in Botswana. Data from the Botswana Acquired Immunodeficiency Syndrome (AIDS) Impact Survey IV (BIAS IV), a nationally representative household-based survey, were used for this study. Multivariable logistic regression and Kulldorf’s scan statistics were used to identify the risk factors and HIV clusters. Socio-demographic characteristics were compared within and outside the clusters. HIV prevalence among the study participants was 25.1% (95% CI 23.3–26.4). HIV infection was significantly higher among the female gender, those older than 24 years and those reporting the use of condoms, while tertiary education had a protective effect. Two significant HIV clusters were identified, one located between Selibe-Phikwe and Francistown and another in the Central Mahalapye district. Clusters had higher levels of unemployment, less people with tertiary education and more people residing in rural areas compared to regions outside the clusters. Our study identified high-risk populations and regions with a high burden of HIV infection in Botswana. This calls for focused innovative and cost-effective HIV interventions on these vulnerable populations and regions to curb the HIV epidemic in Botswana. Full article

(This article belongs to the Special Issue Spatial Epidemiology of Emerging Infectious Diseases)

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13 pages, 2151 KiB

Open AccessArticle

Epidemiological Analysis of the 2019 Dengue Epidemic in Bhutan

by Tsheten Tsheten, Angus Mclure, Archie C. A. Clements, Darren J. Gray, Tenzin Wangdi, Sonam Wangchuk and Kinley Wangdi

Int. J. Environ. Res. Public Health 2021, 18(1), 354; https://doi.org/10.3390/ijerph18010354 - 05 Jan 2021

Cited by 8 | Viewed by 4383

Abstract

Bhutan experienced its largest and first nation-wide dengue epidemic in 2019. The cases in 2019 were greater than the total number of cases in all the previous years. This study aimed to characterize the spatiotemporal patterns and effective reproduction number of this explosive epidemic. Weekly notified dengue cases were extracted from the National Early Warning, Alert, Response and Surveillance (NEWARS) database to describe the spatial and temporal patterns of the epidemic. The time-varying, temperature-adjusted cohort effective reproduction number was estimated over the course of the epidemic. The dengue epidemic occurred between 29 April and 8 December 2019 over 32 weeks, and included 5935 cases. During the epidemic, dengue expanded from six to 44 subdistricts. The effective reproduction number was <3 for most of the epidemic period, except for a ≈1 month period of explosive growth, coinciding with the monsoon season and school vacations, when the effective reproduction number peaked >30 and after which the effective reproduction number declined steadily. Interventions were only initiated 6 weeks after the end of the period of explosive growth. This finding highlights the need to reinforce the national preparedness plan for outbreak response, and to enable the early detection of cases and timely response. Full article

(This article belongs to the Special Issue Spatial Epidemiology of Emerging Infectious Diseases)

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