Inference in epidemiological agent-based models using ensemble-based data assimilation.

To represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous works. The models used are mostly compartmental models representing the mean field evolution through ordinary differential equations.

Vector-Borne Disease Models with Active and Inactive Vectors: A Simple Way to Consider Biting Behavior.

Vector-borne diseases are a serious public health problem, mosquitoes being one of the most important vectors. To analyze the dynamics of this type of disease, Ross-Macdonald models are commonly used. In its simplest formulation and the most common in scientific literature, it is assumed that all mosquitoes are biting at a given rate.

Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation.

Introduction: The objective of this cross-sectional study was to describe the main symptoms associated with COVID-19, and their diagnostic characteristics, to aid in the clinical diagnosis.

Methods: An analysis of all patients diagnosed by RT-PCR for SARS-CoV-2 between April and May 2020 in Argentina was conducted. The data includes clinical and demographic information from all subjects at the time of presentation (n=67318, where 12% were positive for SARS-CoV-2).

Ross-Macdonald models: Which one should we use?

Ross-Macdonald models are the building blocks of most vector-borne disease models. Even for the same disease, different authors use different model formulations, but a study of the dynamical consequences of assuming different hypotheses is missing. In this work we present different formulations of the basic Ross-Macdonald model together with a careful discussion of the assumptions behind each model.