Spatial Infectious Disease Epidemiology

A special issue of Tropical Medicine and Infectious Disease (ISSN 2414-6366). This special issue belongs to the section "Infectious Diseases".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 3872

Special Issue Editor

1. School of Public Health, Curtin University, Perth 6102, Australia
2. Geospital and Tuberculosis (GeoTB) Team, Telethon Kids Institute, Perth 6009, Australia
Interests: spatial epidemiology; infectious diseases; emerging and re-emerging infectious diseases; tuberculosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Knowledge of the geographical distribution of infectious diseases and factors that determine where and when these infectious diseases occur is crucial for policymakers to implement targeted interventions at local and sub-national levels. This might be particularly important in resource-constrained settings and high infectious diseases burden countries. Spatial analyses are useful methods for explaining and predicting patterns of infectious diseases across geographical space and identifying areas of potentially high risk.

Spatial infectious disease epidemiology is an emerging field in epidemiology that includes a variety of analytical approaches to investigate the spatial distribution of infectious diseases, such as bacterial, viral and parasitic diseases, and their demographic, environmental, climatic, behavioural and socioeconomic risk factors. Although spatial epidemiology has a long history of use in public health, it is only recently that spatial analysis has become widely available because of the development of new technologies and the availability of geographical information systems.

The Special Issue “Spatial Infectious Disease Epidemiology” welcomes high-quality original research and review articles in the broad subject area of spatial or spatiotemporal analysis/modelling of bacterial, viral and parasitic diseases, including tuberculosis, HIV, malaria, dengue, cholera, measles, schistosomiasis, leptospirosis, influenza, chikungunya, Zika, Ebola, MERS-CoV, SARS, COVID -19, hepatitis B, hepatitis C, soil-transmitted helminths, neglected tropical diseases, sexually transmitted diseases and other emerging and re-emerging infectious diseases. We are particularly interested in papers focusing on geospatial analyses of data, models explaining spatial or spatiotemporal patterns of infectious diseases and analyses that incorporate a spatial component within the model. 

Dr. Kefyalew Addis Alene
Guest Editor

Manuscript Submission Information

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Keywords

  • spatial epidemiology
  • spatial analysis
  • geospatial analysis
  • spatiotemporal model
  • spatial information in health
  • disease mapping
  • infectious diseases
  • tuberculosis
  • HIV
  • Malaria
  • Dengue
  • Cholera
  • measles
  • Leptospirosis
  • influenza
  • Chikungunya
  • Zika
  • Ebola
  • MERS-CoV
  • SARS
  • COVID-19
  • Hepatitis B
  • Hepatitis C
  • soil-transmitted helminths
  • neglected tropical diseases
  • sexually transmitted diseases
  • emerging and re-emerging infectious diseases

Published Papers (2 papers)

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Research

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10 pages, 2893 KiB  
Article
Impact of COVID-19 Vaccination Rates and Public Measures on Case Rates at the Provincial Level, Thailand, 2021: Spatial Panel Model Analyses
by Charuttaporn Jitpeera, Suphanat Wongsanuphat, Panithee Thammawijaya, Chaninan Sonthichai, Sopon Iamsirithaworn and Scott J. N. McNabb
Trop. Med. Infect. Dis. 2023, 8(6), 311; https://doi.org/10.3390/tropicalmed8060311 - 06 Jun 2023
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Abstract
The coronavirus disease of 2019 (COVID-19) was a pandemic that caused high morbidity and mortality worldwide. The COVID-19 vaccine was expected to be a game-changer for the pandemic. This study aimed to describe the characteristics of COVID-19 cases and vaccination in Thailand during [...] Read more.
The coronavirus disease of 2019 (COVID-19) was a pandemic that caused high morbidity and mortality worldwide. The COVID-19 vaccine was expected to be a game-changer for the pandemic. This study aimed to describe the characteristics of COVID-19 cases and vaccination in Thailand during 2021. An association between vaccination and case rates was estimated with potential confounders at ecological levels (color zones, curfews set by provincial authorities, tourism, and migrant movements) considering time lags at two, four, six, and eight weeks after vaccination. A spatial panel model for bivariate data was used to explore the relationship between case rates and each variable and included only a two-week lag after vaccination for each variable in the multivariate analyses. In 2021, Thailand had 1,965,023 cumulative cases and 45,788,315 total administered first vaccination doses (63.60%). High cases and vaccination rates were found among 31–45-year-olds. Vaccination rates had a slightly positive association with case rates due to the allocation of hot-spot pandemic areas in the early period. The proportion of migrants and color zones measured had positive associations with case rates at the provincial level. The proportion of tourists had a negative association. Vaccinations should be provided to migrants, and collaboration between tourism and public health should prepare for the new era of tourism. Full article
(This article belongs to the Special Issue Spatial Infectious Disease Epidemiology)
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10 pages, 3418 KiB  
Brief Report
Spatio-Temporal Determinants of Dengue Epidemics in the Central Region of Burkina Faso
by Cheick Ahmed Ouattara, Tiandiogo Isidore Traore, Boukary Ouedraogo, Bry Sylla, Seydou Traore, Clement Ziemle Meda, Ibrahim Sangare and Leon Blaise G. Savadogo
Trop. Med. Infect. Dis. 2023, 8(11), 482; https://doi.org/10.3390/tropicalmed8110482 - 25 Oct 2023
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Abstract
The aim of this study was to analyze the spatio-temporal distribution and determinants of the 2017 dengue epidemic in Burkina Faso. A principal component analysis of meteorological and environmental factors was performed to reduce dimensions and avoid collinearities. An initial generalized additive model [...] Read more.
The aim of this study was to analyze the spatio-temporal distribution and determinants of the 2017 dengue epidemic in Burkina Faso. A principal component analysis of meteorological and environmental factors was performed to reduce dimensions and avoid collinearities. An initial generalized additive model assessed the impact of the components derived from this analysis on dengue incidence. Dengue incidence increased mainly with relative humidity, precipitation, normalized difference vegetation index and minimum temperature with an 8-week lag. A Kulldoff Satscan scan was used to identify high-risk dengue clusters, and a second generalized additive model assessed the risk of a health area being at high risk according to land-use factors. The spatio-temporal distribution of dengue fever was heterogeneous and strongly correlated with meteorological factors. The rural communes of Sabaa and Koubri were the areas most at risk. This study provides useful information for planning targeted dengue control strategies in Burkina Faso. Full article
(This article belongs to the Special Issue Spatial Infectious Disease Epidemiology)
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