A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks

Epidemics are often triggered by specific weather patterns favouring the pathogen on susceptible hosts. For plant diseases, models predicting epidemics have therefore often emphasized the identification of early season weather patterns that are correlated with a disease outcome at some later point. Toward that end, window-pane analysis is an exhaustive search algorithm traditionally used in plant pathology for mining correlations in a weather series with respect to a disease endpoint. Here we show, with reference to Fusarium head blight (FHB) of wheat, that a functional approach is a more principled analytical method for understanding the relationship between disease epidemics and environmental conditions over an extended time series. We used scalar-on-function regression to model a binary outcome (FHB epidemic or non-epidemic) relative to weather time series spanning 140 days relative to flowering (when FHB infection primarily occurs). The functional models overall fit the data better than previously described standard logistic regression (lr) models. Periods much earlier than heretofore realized were associated with FHB epidemics. The findings were used to create novel weather summary variables which, when incorporated into lr models, yielded a new set of models that performed as well as existing lr models for real-time predictions of disease risk. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

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Quantifying the hidden costs of imperfect detection for early detection surveillance

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2018.0261 ◽

2019 ◽

Vol 374 (1776) ◽

pp. 20180261 ◽

Cited By ~ 11

Author(s):

Alexander J. Mastin ◽

Frank van den Bosch ◽

Femke van den Berg ◽

Stephen R. Parnell

Keyword(s):

Infectious Disease ◽

Early Detection ◽

Test Performance ◽

Performance Test ◽

Disease Outbreaks ◽

Phytophthora Ramorum ◽

Detection Methods ◽

Effective Control ◽

Theme Issue ◽

Infectious Disease Outbreaks

The global spread of pathogens poses an increasing threat to health, ecosystems and agriculture worldwide. As early detection of new incursions is key to effective control, new diagnostic tests that can detect pathogen presence shortly after initial infection hold great potential for detection of infection in individual hosts. However, these tests may be too expensive to be implemented at the sampling intensities required for early detection of a new epidemic at the population level. To evaluate the trade-off between earlier and/or more reliable detection and higher deployment costs, we need to consider the impacts of test performance, test cost and pathogen epidemiology. Regarding test performance, the period before new infections can be first detected and the probability of detecting them are of particular importance. We propose a generic framework that can be easily used to evaluate a variety of different detection methods and identify important characteristics of the pathogen and the detection method to consider when planning early detection surveillance. We demonstrate the application of our method using the plant pathogen Phytophthora ramorum in the UK, and find that visual inspec-tion for this pathogen is a more cost-effective strategy for early detection surveillance than an early detection diagnostic test. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

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Infectious disease outbreaks in the African region: overview of events reported to the World Health Organization in 2018

Epidemiology and Infection ◽

10.1017/s0950268819001912 ◽

2019 ◽

Vol 147 ◽

Cited By ~ 6

Author(s):

F. Mboussou ◽

P. Ndumbi ◽

R. Ngom ◽

Z. Kassamali ◽

O. Ogundiran ◽

...

Keyword(s):

Infectious Disease ◽

Disease Onset ◽

Disease Outbreaks ◽

Case Fatality ◽

World Health ◽

African Region ◽

Regional Office ◽

Member States ◽

Infectious Disease Outbreaks ◽

The World

Abstract The WHO African region is characterised by the largest infectious disease burden in the world. We conducted a retrospective descriptive analysis using records of all infectious disease outbreaks formally reported to the WHO in 2018 by Member States of the African region. We analysed the spatio-temporal distribution, the notification delay as well as the morbidity and mortality associated with these outbreaks. In 2018, 96 new disease outbreaks were reported across 36 of the 47 Member States. The most commonly reported disease outbreak was cholera which accounted for 20.8% (n = 20) of all events, followed by measles (n = 11, 11.5%) and Yellow fever (n = 7, 7.3%). About a quarter of the outbreaks (n = 23) were reported following signals detected through media monitoring conducted at the WHO regional office for Africa. The median delay between the disease onset and WHO notification was 16 days (range: 0–184). A total of 107 167 people were directly affected including 1221 deaths (mean case fatality ratio (CFR): 1.14% (95% confidence interval (CI) 1.07%–1.20%)). The highest CFR was observed for diseases targeted for eradication or elimination: 3.45% (95% CI 0.89%–10.45%). The African region remains prone to outbreaks of infectious diseases. It is therefore critical that Member States improve their capacities to rapidly detect, report and respond to public health events.

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