Trends in Cholera Epidemiology

Codeço and Coelho discuss a new study on cholera modeling that helps us to better explain the observed epidemic pattern of the disease

COVID-19 and hospitalizations for SARI in Brazil: a comparison up to the 12th epidemiological week of 2020.

Surveillance of the severe acute respiratory illness (SARI) in Brazil aims to characterize the circulation of the Influenza A and B viruses in hospitalized cases and deaths, having been expanded in 2012 to include other respiratory viruses. COVID-19 was detected in Brazil for the time in the 9th epidemiological week of 2020, and the test for the SARS-CoV-2 virus was included in the surveillance protocol starting in the 12th epidemiological week. This study's objective was to investigate the pattern of hospitalizations for SARI in Brazil since the entry of SARS-CoV-2, comparing the temporal and age profiles and laboratory results to the years 2010 through 2019. In 2020, hospitalizations for SARI, compiled from the date of the first confirmed case of COVID-19 up to the 12th week, exceeded the numbers observed during the same period in each of the previous 10 years. The age bracket over 60 years was the most heavily affected, at higher than historical levels. There was a considerable increase in negative laboratory tests, suggesting circulation of a different virus from those already present in the panel. We concluded that the increase in hospitalizations for SARI, the lack of specific information on the etiological agent, and the predominance of cases among the elderly during the same period in which there was an increase in the number of new cases of COVID-19 are all consistent with the hypothesis that severe cases of COVID-19 are already being detected by SARI surveillance, placing an overload on the health system. The inclusion of testing for SARS-CoV-2 in the SARI surveillance protocol and the test's effective nationwide deployment are extremely important for monitoring the evolution of severe COVID-19 cases in Brazil

A modelling approach for correcting reporting delays in disease surveillance data.

One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil

Development, environmental degradation, and disease spread in the Brazilian Amazon.

The Amazon is Brazil's greatest natural resource and invaluable to the rest of the world as a buffer against climate change. The recent election of Brazil's president brought disputes over development plans for the region back into the spotlight. Historically, the development model for the Amazon has focused on exploitation of natural resources, resulting in environmental degradation, particularly deforestation. Although considerable attention has focused on the long-term global cost of "losing the Amazon," too little attention has focused on the emergence and reemergence of vector-borne diseases that directly impact the local population, with spillover effects to other neighboring areas. We discuss the impact of Amazon development models on human health, with a focus on vector-borne disease risk. We outline policy actions that could mitigate these negative impacts while creating opportunities for environmentally sensitive economic activities

Forcing Versus Feedback: Epidemic Malaria and Monsoon Rains in Northwest India

Malaria epidemics in regions with seasonal windows of transmission can vary greatly in size from year to year. A central question has been whether these interannual cycles are driven by climate, are instead generated by the intrinsic dynamics of the disease, or result from the resonance of these two mechanisms. This corresponds to the more general inverse problem of identifying the respective roles of external forcings vs. internal feedbacks from time series for nonlinear and noisy systems. We propose here a quantitative approach to formally compare rival hypotheses on climate vs. disease dynamics, or external forcings vs. internal feedbacks, that combines dynamical models with recently developed, computational inference methods. The interannual patterns of epidemic malaria are investigated here for desert regions of northwest India, with extensive epidemiological records for Plasmodium falciparum malaria for the past two decades. We formulate a dynamical model of malaria transmission that explicitly incorporates rainfall, and we rely on recent advances on parameter estimation for nonlinear and stochastic dynamical systems based on sequential Monte Carlo methods. Results show a significant effect of rainfall in the inter-annual variability of epidemic malaria that involves a threshold in the disease response. The model exhibits high prediction skill for yearly cases in the malaria transmission season following the monsoonal rains. Consideration of a more complex model with clinical immunity demonstrates the robustness of the findings and suggests a role of infected individuals that lack clinical symptoms as a reservoir for transmission. Our results indicate that the nonlinear dynamics of the disease itself play a role at the seasonal, but not the interannual, time scales. They illustrate the feasibility of forecasting malaria epidemics in desert and semi-arid regions of India based on climate variability. This approach should be applicable to malaria in other locations, to other infectious diseases, and to other nonlinear systems under forcing