Climate services for health: from global observations to local interventions

Despite the wealth of available climate data available, there is no consensus on the most appropriate product choice for health impact modelling and how this influences downstream climate-health decisions. We discuss challenges related to product choice, highlighting the importance of considering data biases and co-development of climate services between different sectors

Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models

Recent epidemics of Zika, dengue, and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae. albopictus mosquitoes. We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika, chikungunya, and dengue change with mean temperature, and we show that these predictions are well matched by human case data. Across all three viruses, models and human case data both show that transmission occurs between 18–34°C with maximal transmission occurring in a range from 26–29°C. Controlling for population size and two socioeconomic factors, temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence. Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission, but transmission in temperate areas is limited to at most three months per year even if vectors are present. Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones

An open challenge to advance probabilistic forecasting for dengue epidemics

This is the final version. Available on open access from the National Academy of Sciences via the DOI in this recordData Availability: Data deposition: The data are available at https://github.com/cdcepi/dengue-forecasting-project-2015 (DOI: https://doi.org/10.5281/zenodo.3519270).A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue

Increasing the number of diagnostic mutations in malignant hyperthermia

Malignant hyperthermia (MH) is an autosomal dominant disorder characterized by abnormal calcium homeostasis in skeletal muscle in response to triggering agents. Today, genetic investigations on ryanodine receptor type 1 (RYR1) gene and alpha1 subunit of the dihydropyridine receptor (DHPR) (CACNA1S) gene have improved the procedures associated with MH diagnosis. In approximately 50% of MH cases a causative RYR1 mutation was found. Molecular genetic testing based on RYR1 mutations for MH diagnosis is challenging, because the causative mutations, most of which are private, are distributed throughout the RYR1 gene. A more comprehensive genetic testing procedure is needed. Therefore, we aim to expand the genetic information related to MH and to evaluate the effect of mutations on the MH phenotype. Performing an in-depth mutation screening of the RYR1 transcript sequence in 36 unrelated MH susceptible (MHS) patients, we identified 17 novel, five rare, and eight non-disease-causing variants in 23 patients. The 13 remaining MHS patients presented no known variants, neither in RYR1 nor in the CACNA1S binding regions to RYR1. The 17 novel variants were found to affect highly conserved amino acids and were absent in 100 controls. Excellent genotype-phenotype correlations were found by investigating 21 MHS families-a total of 186 individuals. Epstein-Barr virus (EBV) lymphoblastoid cells carrying four of these novel mutations showed abnormal calcium homeostasis. The results of this study contribute to the establishment of a robust genetic testing procedure for MH diagnosis