Predicting Crimean-Congo Hemorrhagic Fever Outbreaks via Multivariate Time-Series Classification of Climate Data

Jonathan Harris; Thilanka Munasinghe; Heidi Tubbs; Assaf Anyamba

doi:10.1145/3545729.3545772

Predicting Crimean-Congo Hemorrhagic Fever Outbreaks via Multivariate Time-Series Classification of Climate Data

Jonathan Harris, Thilanka Munasinghe, Heidi Tubbs, Assaf Anyamba

Remote Sensing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Crimean-Congo hemorrhagic fever (CCHF) is a vector-borne disease that is spread by ticks (specifically of the Hyalomma marginatum species) and is influenced by climate patterns. CCHF has a fatality rate ranging from 3-50% for humans and is a high-priority disease among international health organizations. We hypothesize that temporal variability in climate variables (temperature and precipitation) can be used to predict CCHF outbreaks in a particular region. There is a need to analyze the effects of climatic patterns on the spread of CCHF to allow high-risk countries to better prepare for possible outbreaks. We propose an approach that utilizes multivariate time-series classification (MTSC) to detect temporal climatic patterns and predicts reports of CCHF outbreaks within Pakistan with a 91.5% test accuracy.

Original language	English
Title of host publication	ICMHI 2022 - 2022 6th International Conference on Medical and Health Informatics
Publisher	Association for Computing Machinery
Pages	215-218
Number of pages	4
ISBN (Electronic)	9781450396301
DOIs	https://doi.org/10.1145/3545729.3545772
State	Published - May 15 2022
Event	6th International Conference on Medical and Health Informatics, ICMHI 2022 - Virtual, Online, Japan Duration: May 12 2022 → May 15 2022

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	6th International Conference on Medical and Health Informatics, ICMHI 2022
Country/Territory	Japan
City	Virtual, Online
Period	05/12/22 → 05/15/22

Funding

The data used in this effort were acquired as part of the activities of NASA’s Science Mission Directorate, and are archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC). Analyses and visualizations used in this paper were produced with the Giovanni online data system, developed and maintained by the NASA GES DISC. This was work conducted as part of Group on Earth Observations (GEO) Health Community of Practice (CoP) activities for Student engagement under TM and AA. HT & AA were supported under funding from NASA Applied Sciences Program – Health and Air Quality, Grant #17-HAQ17-0065. We would like to express our gratitude to the ITWS program at Rensselaer Polytechnic Institute for their support during this project.

Keywords

Crimean-Congo hemorrhagic fever
Hyalomma marginatum
climate variability
datasets
infectious diseases
neural networks
tick-borne diseases
vector-borne diseases

Access to Document

10.1145/3545729.3545772

Cite this

Harris, J., Munasinghe, T., Tubbs, H., & Anyamba, A. (2022). Predicting Crimean-Congo Hemorrhagic Fever Outbreaks via Multivariate Time-Series Classification of Climate Data. In ICMHI 2022 - 2022 6th International Conference on Medical and Health Informatics (pp. 215-218). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3545729.3545772

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title = "Predicting Crimean-Congo Hemorrhagic Fever Outbreaks via Multivariate Time-Series Classification of Climate Data",

abstract = "Crimean-Congo hemorrhagic fever (CCHF) is a vector-borne disease that is spread by ticks (specifically of the Hyalomma marginatum species) and is influenced by climate patterns. CCHF has a fatality rate ranging from 3-50\% for humans and is a high-priority disease among international health organizations. We hypothesize that temporal variability in climate variables (temperature and precipitation) can be used to predict CCHF outbreaks in a particular region. There is a need to analyze the effects of climatic patterns on the spread of CCHF to allow high-risk countries to better prepare for possible outbreaks. We propose an approach that utilizes multivariate time-series classification (MTSC) to detect temporal climatic patterns and predicts reports of CCHF outbreaks within Pakistan with a 91.5\% test accuracy.",

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Harris, J, Munasinghe, T, Tubbs, H & Anyamba, A 2022, Predicting Crimean-Congo Hemorrhagic Fever Outbreaks via Multivariate Time-Series Classification of Climate Data. in ICMHI 2022 - 2022 6th International Conference on Medical and Health Informatics. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 215-218, 6th International Conference on Medical and Health Informatics, ICMHI 2022, Virtual, Online, Japan, 05/12/22. https://doi.org/10.1145/3545729.3545772

Predicting Crimean-Congo Hemorrhagic Fever Outbreaks via Multivariate Time-Series Classification of Climate Data. / Harris, Jonathan; Munasinghe, Thilanka; Tubbs, Heidi et al.
ICMHI 2022 - 2022 6th International Conference on Medical and Health Informatics. Association for Computing Machinery, 2022. p. 215-218 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2022/5/15

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Harris J, Munasinghe T, Tubbs H, Anyamba A. Predicting Crimean-Congo Hemorrhagic Fever Outbreaks via Multivariate Time-Series Classification of Climate Data. In ICMHI 2022 - 2022 6th International Conference on Medical and Health Informatics. Association for Computing Machinery. 2022. p. 215-218. (ACM International Conference Proceeding Series). doi: 10.1145/3545729.3545772

Predicting Crimean-Congo Hemorrhagic Fever Outbreaks via Multivariate Time-Series Classification of Climate Data

Abstract

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Keywords

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