Tropical Medicine and Infectious Disease

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Special Issue Editors

Dr. Amalie Dyda

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Guest Editor

School of Public Health, Faculty of Medicine, University of Queensland, Herston, QLD, Australia
Interests: infectious diseases; vaccination; social media; machine learning; public health; informatics

Dr. Anthony Nguyen

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Guest Editor

The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, QLD, Australia
Interests: health informatics; data analytics; machine learning; e-Health decision support; digital public health surveillance

Special Issue Information

Dear Colleagues,

Infectious diseases cause significant health, social, and economic impacts each year, with malaria, tuberculosis, and HIV/AID consistently listed in the top 10 causes of death worldwide. As well as the ongoing impact, infectious diseases can lead to large-scale pandemics, as highlighted by COVID-19, which has led to more than 6 million deaths since 2020.

Artificial intelligence (AI), machine learning, and novel data analytics offer new methods of data analysis which may assist in the monitoring and control of infectious diseases. Specifically, these methods have shown to be useful in the prediction and detection of infectious diseases, as well identifying those most at risk of disease. These methods allow the use of large datasets that have been underutilised with traditional surveillance and epidemiological methods. Data used to date includes hospitalisations, genomic data and other health datasets, demographic data, social media data, and spatial data.

This Special Issue will focus on highlighting novel applications of AI and machine learning to improve the investigation and control of infectious diseases. We invite submissions from researchers regarding novel uses of AI and machine learning in infectious disease investigation and control, as well as review articles discussing future opportunities and challenges in this emerging area of research.

Dr. Amalie Dyda
Dr. Anthony Nguyen
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. Tropical Medicine and Infectious Disease 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 2700 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

infectious diseases
machine learning
artificial intelligence
surveillance
epidemiology
clinical decision support

Published Papers (2 papers)

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Research

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22 pages, 3112 KiB

Open AccessArticle

Assessment Model for Rapid Suppression of SARS-CoV-2 Transmission under Government Control

by Lihu Pan, Ya Su, Huimin Yan and Rui Zhang

Trop. Med. Infect. Dis. 2022, 7(12), 399; https://doi.org/10.3390/tropicalmed7120399 - 25 Nov 2022

Cited by 1 | Viewed by 1166

Abstract

The rapid suppression of SARS-CoV-2 transmission remains a priority for maintaining public health security throughout the world, and the agile adjustment of government prevention and control strategies according to the spread of the epidemic is crucial for controlling the spread of the epidemic. Thus, in this study, a multi-agent modeling approach was developed for constructing an assessment model for the rapid suppression of SARS-CoV-2 transmission under government control. Different from previous mathematical models, this model combines computer technology and geographic information system to abstract human beings in different states into micro-agents with self-control and independent decision-making ability; defines the rules of agent behavior and interaction; and describes the mobility, heterogeneity, contact behavior patterns, and dynamic interactive feedback mechanism of space environment. The real geospatial and social environment in Taiyuan was considered as a case study. In the implemented model, the government agent could adjust the response level and prevention and control policies for major public health emergencies in real time according to the development of the epidemic, and different intervention strategies were provided to improve disease control methods in the simulation experiment. The simulation results demonstrate that the proposed model is widely applicable, and it can not only judge the effectiveness of intervention measures in time but also analyze the virus transmission status in complex urban systems and its change trend under different intervention measures, thereby providing scientific guidance to support urban public health safety. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Analytics for Infectious Diseases Investigation and Control)

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10 pages, 2394 KiB

Open AccessBrief Report

Wastewater Surveillance Can Function as an Early Warning System for COVID-19 in Low-Incidence Settings

by Mohamad Assoum, Colleen L. Lau, Phong K. Thai, Warish Ahmed, Jochen F. Mueller, Kevin V. Thomas, Phil Min Choi, Greg Jackson and Linda A. Selvey

Trop. Med. Infect. Dis. 2023, 8(4), 211; https://doi.org/10.3390/tropicalmed8040211 - 31 Mar 2023

Cited by 4 | Viewed by 1905

Abstract

Introduction: During the first two years of the COVID-19 pandemic, Australia implemented a series of international and interstate border restrictions. The state of Queensland experienced limited COVID-19 transmission and relied on lockdowns to stem any emerging COVID-19 outbreaks. However, early detection of new outbreaks was difficult. In this paper, we describe the wastewater surveillance program for SARS-CoV-2 in Queensland, Australia, and report two case studies in which we aimed to assess the potential for this program to provide early warning of new community transmission of COVID-19. Both case studies involved clusters of localised transmission, one originating in a Brisbane suburb (Brisbane Inner West) in July–August 2021, and the other originating in Cairns, North Queensland in February–March 2021. Materials and Methods: Publicly available COVID-19 case data derived from the notifiable conditions (NoCs) registry from the Queensland Health data portal were cleaned and merged spatially with the wastewater surveillance data using statistical area 2 (SA2) codes. The positive predictive value and negative predictive value of wastewater detection for predicting the presence of COVID-19 reported cases were calculated for the two case study sites. Results: Early warnings for local transmission of SARS-CoV-2 through wastewater surveillance were noted in both the Brisbane Inner West cluster and the Cairns cluster. The positive predictive value of wastewater detection for the presence of notified cases of COVID-19 in Brisbane Inner West and Cairns were 71.4% and 50%, respectively. The negative predictive value for Brisbane Inner West and Cairns were 94.7% and 100%, respectively. Conclusions: Our findings highlight the utility of wastewater surveillance as an early warning tool in low COVID-19 transmission settings. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Analytics for Infectious Diseases Investigation and Control)

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