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

Bracher, Johannes; Held, Leonhard

doi:10.1111/biom.13371

Cited by 20 publications

(26 citation statements)

References 50 publications

(89 reference statements)

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“…In the present analysis, we could not disentangle asymptomatic and symptomatic disease. Under-reporting can introduce artifacts in the autocorrelation structure and may confound the estimation of lag weights of the underlying serial interval distribution ( 52 ). Additionally, we assumed that model coefficients were constant over time.…”

Section: Discussionmentioning

confidence: 99%

“…We began with the first-order autoregressive modeling ( D = 1 in (1)) of daily COVID-19 incidence using intercept only model population offset and country connectivity. In a mechanistic interpretation of such a first-order model, the time between the appearance of symptoms in successive generations is assumed to be fixed to the observation interval at which the data are collected, here as one day ( 52 ).…”

Section: Methodsmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Tracking and predicting the African COVID-19 pandemic

Ssentongo

Fronterrè

Geronimo

et al. 2020

Preprint

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“…All models were fit using the R package surveillance [32] and its extension hhh4addon [33] in R version 3.5.1 (2018-07-02) [34].…”

Section: Methodsmentioning

confidence: 99%

A Spatio-temporal Approach to Short-Term Prediction of Visceral Leishmaniasis Diagnoses in India

Nightingale

Chapman

Srikantiah

et al. 2019

Preprint

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Background: The elimination programme for visceral leishmaniasis (VL) in India has seen great progress, with total cases decreasing by over 80% since 2010 and many blocks now reporting zero cases from year to year. Prompt diagnosis and treatment is critical to continue progress and avoid epidemics in the increasingly susceptible population. Short-term forecasts could be used to highlight anomalies in incidence and support health service logistics. The model which best fits the data is not necessarily most useful for prediction, yet little empirical work has been done to investigate the balance between fit and predictive performance. Principal Findings: We developed statistical models of monthly VL case counts at block level. By evaluating a set of randomly-generated models, we found that fit and one-month-ahead prediction were strongly correlated and that rolling updates to model parameters as data accrued were not crucial for accurate prediction. The final model incorporated auto-regression over four months, spatial correlation between neighbouring blocks,and seasonality. Ninety-four percent of 10-90% prediction intervals from this model captured the observed count during a 24-month test period. Comparison of one-, three- and four-month-ahead predictions from the final model fit demonstrated that a longer time horizon yielded only a small sacrifice in predictive power for the vast majority of blocks. Conclusions: The model developed is informed by routinely-collected surveillance data as it accumulates, and predictions are sufficiently accurate and precise to be useful. Such forecasts could, for example, be used to guide stock requirements for rapid diagnostic tests and drugs. More comprehensive data on factors thought to influence geographic variation in VL burden could be incorporated, and might better explain the heterogeneity between blocks and improve uniformity of predictive performance.Integration of the approach in the management of the VL programme would be an important step to ensuring continued successful control. and processing of the data,predictive power more than three months ahead of current data is crucial to make forecasts which can feasibly be acted upon. Some heterogeneity remains in predictive power across the study region which could potentially be improved using unit-specific data on factors believed to be associated with reported VL incidence (e.g. age distribution, socio-economic status and climate)

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“…In the present analysis, we could not disentangle asymptomatic and symptomatic disease. Underreporting can introduce artifacts in the autocorrelation structure and may confound the estimation of lag weights of the underlying serial interval distribution ( 52 ).…”

Section: Discussionmentioning

confidence: 99%

“…We began with the first-order autoregressive modeling (

1

in [ 1 ]) of daily COVID-19 incidence using intercept-only model population offset and country connectivity. In a mechanistic interpretation of such a first-order model, the time between the appearance of symptoms in successive generations is assumed to be fixed to the observation interval at which the data are collected, here as 1 d ( 52 ).…”

Section: Methodsmentioning

confidence: 99%

Pan-African evolution of within- and between-country COVID-19 dynamics

Ssentongo

Fronterrè

Geronimo

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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The 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 created a strategy that helps predict the country-level case occurrences based on cases within or external to a country throughout 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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A marginal moment matching approach for fitting endemic‐epidemic models to underreported disease surveillance counts

Cited by 20 publications

References 50 publications

Tracking and predicting the African COVID-19 pandemic

Tracking and predicting the African COVID-19 pandemic

A Spatio-temporal Approach to Short-Term Prediction of Visceral Leishmaniasis Diagnoses in India

Pan-African evolution of within- and between-country COVID-19 dynamics

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