Technology to advance infectious disease forecasting for outbreak management

ABSTRACTUnderstanding pathogen outbreak and emergence events has important implications to the management of infectious disease. Apart from preempting infectious disease events, there is considerable interest in determining why certain pathogens are consistently found in some regions, and why others spontaneously emerge or reemerge over time. Here, we use a trait-free approach which leverages information on the global community of human infectious diseases to estimate the potential for pathogen outbreak, emergence, and re-emergence events over time. Our approach uses pairwise dissimilarities among pathogen distributions between countries and country-level pathogen composition to quantify pathogen outbreak, emergence, and re-emergence potential as a function of time (e.g., number of years between training and prediction), pathogen type (e.g., virus), and transmission mode (e.g., vector-borne). We find that while outbreak and re-emergence potential are well captured by our simple model, prediction of emergence events remains elusive, and sudden global emergences like an influenza pandemic seem beyond the predictive capacity of the model. While our approach allows for dynamic predictability of outbreak and re-emergence events, data deficiencies and the stochastic nature of emergence events may preclude accurate prediction. Together, our results make a compelling case for incorporating a community ecological perspective into existing disease forecasting efforts.

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Evaluating epidemic forecasts in an interval format

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008618 ◽

2021 ◽

Vol 17 (2) ◽

pp. e1008618

Author(s):

Johannes Bracher ◽

Evan L. Ray ◽

Tilmann Gneiting ◽

Nicholas G. Reich

Keyword(s):

Infectious Disease ◽

Absolute Error ◽

Forecast Evaluation ◽

Evaluation Metrics ◽

Practical Reasons ◽

Disease Forecasting ◽

Predictive Distributions ◽

Probability Score ◽

Continuous Ranked Probability Score ◽

Probabilistic Forecasts

For practical reasons, many forecasts of case, hospitalization, and death counts in the context of the current Coronavirus Disease 2019 (COVID-19) pandemic are issued in the form of central predictive intervals at various levels. This is also the case for the forecasts collected in the COVID-19 Forecast Hub (https://covid19forecasthub.org/). Forecast evaluation metrics like the logarithmic score, which has been applied in several infectious disease forecasting challenges, are then not available as they require full predictive distributions. This article provides an overview of how established methods for the evaluation of quantile and interval forecasts can be applied to epidemic forecasts in this format. Specifically, we discuss the computation and interpretation of the weighted interval score, which is a proper score that approximates the continuous ranked probability score. It can be interpreted as a generalization of the absolute error to probabilistic forecasts and allows for a decomposition into a measure of sharpness and penalties for over- and underprediction.

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Data–model fusion to better understand emerging pathogens and improve infectious disease forecasting

Ecological Applications ◽

10.1890/09-1409.1 ◽

2011 ◽

Vol 21 (5) ◽

pp. 1443-1460 ◽

Cited By ~ 39

Author(s):

Shannon L. LaDeau ◽

Gregory E. Glass ◽

N. Thompson Hobbs ◽

Andrew Latimer ◽

Richard S. Ostfeld

Keyword(s):

Infectious Disease ◽

Data Model ◽

Disease Forecasting ◽

Emerging Pathogens ◽

Model Fusion

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Mobile-based and open-source infectious disease surveillance and outbreak management in Tanzania

European Journal of Public Health ◽

10.1093/eurpub/ckaa166.1347 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

I Kassim ◽

C Arinze ◽

D Tom-Aba ◽

O Adeoye ◽

C Ihekweazu ◽

...

Keyword(s):

Infectious Disease ◽

Disease Control ◽

Open Source ◽

Real Time ◽

Disease Surveillance ◽

State Of The Art ◽

Infectious Disease Control ◽

Outbreak Response ◽

Infectious Disease Surveillance ◽

Outbreak Management

Abstract Introduction The PANDORA-ID-NET consortium aims to build capacity for effective outbreak response in sub-Saharan Africa. Part of this mission is to develop a real-time data sharing platform for disease outbreaks that leverages centralised data management and uses mobile technologies for data gathering and feedback. We have committed to using open-source technologies, so that the platform can be deployed on regional IT infrastructure and further developed by local staff, and collected data can be stored and processed in the region of origin. This abstract aims to describe how we identified a state of the art open-source system that fulfils these criteria, and the process of how we are extending it to function within the current infectious disease control framework in Tanzania, under our partnership with the Ifakara Health Institute (IHI). Methods To find state of the art open-source systems matching our criteria, we performed a rapid review of the literature. We screened 1022 articles and found 15 candidate systems, out of which only SORMAS satisfied the criteria. SORMAS was developed jointly by the Helmholtz Centre for Infection Research (HZI) and the Nigeria CDC, and was modeled on Nigeria's successful response to the Ebola outbreak. The system can be used for case management, contact tracing, surveillance, and laboratory sample management. Data is collected and synchronised using Android mobile devices (both online and offline) and data aggregation and analysis are performed in real-time via a web application Results Having chosen SORMAS, we established a collaboration between the SORMAS developer team and the PANDORA team. IHI are guiding ongoing work on adapting SORMAS to the Tanzanian health facility geography and the country's case definition guidelines for notifiable diseases. Conclusions Once adapted for Tanzania, SORMAS will fill an unoccupied niche in infectious disease control, improving the quality of collected case data and enabling better outbreak response Key messages A state of the art, mobile-based, open-source outbreak management and infectious disease surveillance system (SORMAS) is being deployed in Tanzania. We outline our experience with piloting SORMAS in Tanzania, building on the experience of our Nigerian and German partners, who rolled out this system nationally in Nigeria and other African countries.

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Mixed infectious disease forecasting technique, based upon seasonal decomposition and Sarima

Journal of microbiology epidemiology immunobiology ◽

10.36233/0372-9311-2019-3-76-83 ◽

2019 ◽

pp. 76-83

Author(s):

E. N. Filatova ◽

L. A. Solntsev

Keyword(s):

Infectious Disease ◽

Disease Forecasting ◽

Seasonal Decomposition

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Mobile-based and open-source case detection and infectious disease outbreak management systems: a review

Wellcome Open Research ◽

10.12688/wellcomeopenres.15723.1 ◽

2020 ◽

Vol 5 ◽

pp. 37 ◽

Cited By ~ 1

Author(s):

Alexei Yavlinsky ◽

Swaib A. Lule ◽

Rachel Burns ◽

Alimuddin Zumla ◽

Timothy D. McHugh ◽

...

Keyword(s):

Infectious Disease ◽

Open Source ◽

Disease Outbreak ◽

Lessons Learned ◽

Sub Saharan Africa ◽

Rapid Review ◽

Management Systems ◽

Infectious Disease Outbreak ◽

Outbreak Management ◽

Sub Saharan

In this paper we perform a rapid review of existing mobile-based, open-source systems for infectious disease outbreak data collection and management. Our inclusion criteria were designed to match the PANDORA-ID-NET consortium’s goals for capacity building in sub-Saharan Africa, and to reflect the lessons learned from the 2014–16 West African Ebola outbreak. We found eight candidate systems that satisfy some or most of these criteria, but only one (SORMAS) fulfils all of them. In addition, we outline a number of desirable features that are not currently present in most outbreak management systems.

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