Stratified Space–Time Infectious Disease Modelling, with an Application to Hand, Foot and Mouth Disease in China

Bauer, Cici; Wakefield, Jon

doi:10.1111/rssc.12284

Cited by 23 publications

(35 citation statements)

References 58 publications

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“…To model the spread of COVID-19 in Africa, we employ a data-driven endemic-epidemic model ( 33 ) to 1) visualize the burden of cases including the proportion of cases arising from sources local within-country and external between-country, 2) describe the factors which most correlate with spread, and 3) enable short-term forecasting of new cases. This modeling framework has been used previously to fit space-time dynamics of COVID-19 in Italy ( 34 ), Germany ( 35 ) and the United Kingdom ( 36 ) and to analyze other infectious diseases ( 37 ). The model is divided into three main parts: two epidemic components that capture sources of infections coming from within the country and from neighboring areas, and an endemic component that includes all contributions to the reported number of cases that are not taken into account by the epidemic part.…”

Section: Introductionmentioning

confidence: 99%

Tracking and predicting the African COVID-19 pandemic

Ssentongo

Fronterrè

Geronimo

et al. 2020

Preprint

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The ongoing coronavirus disease 2019 (COVID-19) pandemic is heterogeneous throughout Africa and threatening millions of lives. Surveillance and short-term modeling forecasts are critical to provide timely information for decisions on control strategies. We use a model that explains the evolution of the COVID-19 pandemic over time in the entire African continent, parameterized by socioeconomic and geoeconomic variations and the lagged effects of social policy and meteorological history. We observed the effect of the human development index, containment policies, testing capacity, specific humidity, temperature and landlocked status of countries on the local within-country and external between-country transmission. One week forecasts of case numbers from the model were driven by the quality of the reported data. Seeking equitable behavioral and social interventions, balanced with coordinated country-specific strategies in infection suppression, should be a continental priority to control the COVID-19 pandemic in Africa.

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Section: Introductionmentioning

confidence: 99%

Tracking and predicting the African COVID-19 pandemic

Ssentongo

Fronterrè

Geronimo

et al. 2020

Preprint

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“…To circumvent this, more sophisticated algorithms seem to be needed. Secondly, prior choice is challenging in EE models (Bauer and Wakefield, 2018). Advantages, however, are that the uncertainty about the reporting probability π could be included more easily and that the hidden process {X t } could be reconstructed.…”

Section: Discussionmentioning

confidence: 99%

“…This model class is of particular interest in epidemiological applications as it can be derived from a mechanistic model of disease transmission (Wakefield et al, 2019) and serve to estimate effective reproductive numbers. The EE class has been used for this purpose by Wang et al (2011) and Bauer and Wakefield (2018), but without accounting for underreporting of surveillance data.…”

Section: Introductionmentioning

confidence: 99%

A marginal moment matching approach for fitting endemic‐epidemic models to underreported disease surveillance counts

Bracher

Held

2020

Biometrics

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Count data are often subject to underreporting, especially in infectious disease surveillance. We propose an approximate maximum likelihood method to fit count time series models from the endemic-epidemic class to underreported data. The approach is based on marginal moment matching where underreported processes are approximated through completely observed processes from the same class. Moreover, the form of the bias when underreporting is ignored or taken into account via multiplication factors is analyzed. Notably, we show that this leads to a downward bias in model-based estimates of the effective reproductive number. A marginal moment matching approach can also be used to account for reporting intervals which are longer than the mean serial interval of a disease. The good performance of the proposed methodology is demonstrated in simulation studies. An extension to time-varying parameters and reporting probabilities is discussed and applied in a case study on weekly rotavirus gastroenteritis counts in Berlin, Germany.

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“…The parameters ν kt , λ kt , and φ kt are constrained to be non-negative and can be modeled by allowing for log-linear predictors in all three components, as sine-cosine terms to account for seasonality [42], long-term temporal trends, or/and covariates [43,44].…”

Section: Endemic-epidemic Models R Package Surveillance [26]mentioning

confidence: 99%

A Review of Spatiotemporal Models for Count Data in R Packages. A Case Study of COVID-19 Data

2021

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Spatiotemporal models for count data are required in a wide range of scientific fields, and they have become particularly crucial today because of their ability to analyze COVID-19-related data. The main objective of this paper is to present a review describing the most important approaches, and we monitor their performance under the same dataset. For this review, we focus on the three R-packages that can be used for this purpose, and the different models assessed are representative of the two most widespread methodologies used to analyze spatiotemporal count data: the classical approach and the Bayesian point of view. A COVID-19-related case study is analyzed as an illustration of these different methodologies. Because of the current urgent need for monitoring and predicting data in the COVID-19 pandemic, this case study is, in itself, of particular importance and can be considered the secondary objective of this work. Satisfactory and promising results have been obtained in this second goal. With respect to the main objective, it has been seen that, although the three models provide similar results in our case study, their different properties and flexibility allow us to choose the model depending on the application at hand.

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Stratified Space–Time Infectious Disease Modelling, with an Application to Hand, Foot and Mouth Disease in China

Cited by 23 publications

References 58 publications

Tracking and predicting the African COVID-19 pandemic

Tracking and predicting the African COVID-19 pandemic

A marginal moment matching approach for fitting endemic‐epidemic models to underreported disease surveillance counts

A Review of Spatiotemporal Models for Count Data in R Packages. A Case Study of COVID-19 Data

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