Bayesian data assimilation provides rapid decision support for vector-borne diseases

  title={Bayesian data assimilation provides rapid decision support for vector-borne diseases},
  author={Christopher P Jewell and Richard G Brown},
  journal={Journal of The Royal Society Interface},
Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide… 

Figures from this paper

Infectious Disease Forecasting for Public Health

This work states that public health surveillance systems and other sources provide valuable data that can be used to accurately forecast disease incidence, but many aspects of common infectious disease surveillance data are imperfect.

The Use and Misuse of Mathematical Modeling for Infectious Disease Policymaking: Lessons for the COVID-19 Pandemic

Key limitations of mathematical modeling as a tool for interpreting empirical data and informing individual and public decision making are reviewed and several approaches that have been used to strengthen the validity of inferences drawn from these analyses are presented.

Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination

The development of an analytical framework to quantify the relative values of various longitudinal infection surveillance data collected in field sites undergoing mass drug administrations for calibrating three lymphatic filariasis (LF) models and for improving their predictions of the required durations of drug interventions to achieve parasite elimination in endemic populations is reported.

Predicting lymphatic filariasis elimination in data-limited settings: A reconstructive computational framework for combining data generation and model discovery

A new data-model computational discovery system that couples data-assimilation methods based on existing monitoring survey data with model-generated data about baseline conditions, in order to discover the local LF transmission models required for simulating the impacts of interventions for achieving parasite elimination in typical endemic locations is presented.

The role of case proximity in transmission of visceral leishmaniasis in a highly endemic village in Bangladesh

Spatially targeting active case detection and/or IRS to higher risk areas would greatly reduce costs of control, but its effectiveness as a control strategy is unknown and the extent to which spatial transmission patterns and the asymptomatic contribution vary with VL endemicity and over time is uncertain.

Compatibility between livestock databases used for quantitative biosecurity response in New Zealand

The three national livestock biosecurity databases in New Zealand cannot be reliably linked together to provide a single picture of New Zealand's livestock industry, limiting the ability to use advanced quantitative techniques to provide effective decision support during disease outbreaks.



Bayesian analysis for emerging infectious diseases

This paper presents a fully Bayesian methodology for performing inference and online prediction for epidemics in structured populations, including an MCMC- (and adaptive MCMC-) based methodology for parameter estimation, epidemic prediction, and online assessment of risk from currently unobserved infections.

Bayesian inference for an emerging arboreal epidemic in the presence of control

The model is modeled as a susceptible-exposed-infectious-detected-removed epidemic, where the exposure and infectious times are not observed, detection times are censored, removal times are known, and the disease is spreading through a heterogeneous host population with trees of different age and susceptibility.

Appropriate Models for the Management of Infectious Diseases

Analytical methods are used to show that ignoring the latent period or making the common assumption of exponentially distributed latent and infectious periods always results in underestimating the basic reproductive ratio of an infection from outbreak data.

A Comparison of Dynamics in Two Models for the Spread of a Vector-Borne Disease.

It is demonstrated how an agent- based model shows greater sensitivity to the level of vaccine uptake and has lower variability compared with a kernel-based model, while a model using a transmission kernel requires less detailed data and is often faster.

Enhancing Bayesian risk prediction for epidemics using contact tracing.

This paper presents an advancement in Bayesian inference for epidemics that assimilates CTD data and is robust to partial contact tracing and shows how the presence of CTD improves posterior predictive accuracy and can directly inform a more effective control strategy.

Towards an integrated approach in surveillance of vector-borne diseases in Europe

In the current paper, important parameters and terms of both public health and medical entomology are defined in order to establish a common language that facilitates collaboration between the two disciplines.

The 2012 Madeira Dengue Outbreak: Epidemiological Determinants and Future Epidemic Potential

It is proposed that there is little support for dengue endemicity on this island, but a high potential for future epidemic outbreaks when seeded between May and August—a period when detection of imported cases is crucial for Madeira's public health planning.

Climate change and the emergence of vector-borne diseases in Europe: case study of dengue fever

This is the first attempt to model dengue fever risk in Europe in terms of disease occurrence rather than mosquito presence, and is likely to be a valuable tool assisting effective and targeted adaptation responses to reduce the likely increased burden of d Dengue fever in a warmer world.

Predicting undetected infections during the 2007 foot-and-mouth disease outbreak

Real-time epidemic data synthesized with previous knowledge of FMD outbreaks in the UK is used to predict which premises might have been infected, but remained undetected, at any point during the outbreak, providing both a framework for targeting surveillance in the face of limited resources and an indicator of the current severity and spatial extent of the epidemic.