Predicting the number of COVID-19 cases from the reported number of deaths

The new corona virus disease 2019 (COVID-2019) is rapidly spreading through the world. With insufficient testing, available case data may underestimate the total number of infections. We statistically estimated the cumulative number of cases with confidence intervals from the reported number of deaths, assuming that the number of infections grow exponentially with a constant doubling time. Our findings indicate that the number of unreported infections is likely at least one order of magnitude higher than the reported cases, in particular in the United States and the United Kingdom. This finding is supported by a recent mass screening for SARS-CoV-2 in Iceland which found that the actual case numbers may be about 20 times higher than the officially reported number

A Method for Estimating the Number of Infections From the Reported Number of Deaths

At the outset of an epidemic, available case data typically underestimate the total number of infections due to insufficient testing, potentially hampering public responses. Here, we present a method for statistically estimating the true number of cases with confidence intervals from the reported number of deaths and estimates of the infection fatality ratio; assuming that the time from infection to death follows a known distribution. While the method is applicable to any epidemic with a significant mortality rate, we exemplify the method by applying it to COVID-19. Our findings indicate that the number of unreported COVID-19 infections in March 2020 was likely to be at least one order of magnitude higher than the reported cases, with the degree of underestimation among the countries considered being particularly high in the United Kingdom

Climate change may enable Aedes aegypti infestation in major European cities by 2100

Background Climate change allows Aedes aegypti to infest new areas. Consequently, it enables the arboviruses the mosquito transmits ­- e.g., dengue, chikungunya, Zika and yellow fever -- to emerge in previously uninfected areas. An example is the Portuguese island of Madeira during 2012-13. Objective We aim to understand how climate change will affect the future spread of this potent vector, as an aid in assessing the risk of disease outbreaks and effectively allocating resources for vector control. Methods We used an empirically-informed, process-based mathematical model to study the feasibility of Aedes aegypti infestation into continental Europe. Based on established global climate-change scenario data, we assess the potential of Aedes aegypti to establish in Europe over the 21st century by estimating the vector population growth rate for five climate models (GCM5). Results In a low carbon emission future (RCP2.6), we find minimal change to the current situation throughout the whole of the 21st century. In a high carbon future (RCP8.5), a large parts of southern Europe risks being invaded by Aedes aegypti. Conclusion Our results show that successfully enforcing the Paris Agreement by limiting global warming to below 2 °C significantly lowers the risk for infestation of Aedes aegypti and consequently of potential large-scale arboviral disease outbreaks in Europe within the 21st century

Geographical differences on the mortality impact of heat waves in Europe

Climate change is potentially the biggest global health threat in the 21st century. Deaths related with heat waves and spread of infectious diseases will be part of the menace though the major impact will be caused by malnutrition, diarrhea and extreme climate events. Consequently, loss of healthy life years as a result of global climate change is predicted to be 500 times greater in poor African populations than in European populations. However, the increase of more than 2°C of average temperature will result in a negative health impact in all regions, the potential benefits of a warmer temperature being negatively compensated, heat waves being one of the largest climate change threats in the developed world

Projecting the risk of mosquito-borne diseases in a warmer and more populated world: a multi-model, multi-scenario intercomparison modelling study.

BACKGROUND: Mosquito-borne diseases are expanding their range, and re-emerging in areas where they had subsided for decades. The extent to which climate change influences the transmission suitability and population at risk of mosquito-borne diseases across different altitudes and population densities has not been investigated. The aim of this study was to quantify the extent to which climate change will influence the length of the transmission season and estimate the population at risk of mosquito-borne diseases in the future, given different population densities across an altitudinal gradient. METHODS: Using a multi-model multi-scenario framework, we estimated changes in the length of the transmission season and global population at risk of malaria and dengue for different altitudes and population densities for the period 1951-99. We generated projections from six mosquito-borne disease models, driven by four global circulation models, using four representative concentration pathways, and three shared socioeconomic pathways. FINDINGS: We show that malaria suitability will increase by 1·6 additional months (mean 0·5, SE 0·03) in tropical highlands in the African region, the Eastern Mediterranean region, and the region of the Americas. Dengue suitability will increase in lowlands in the Western Pacific region and the Eastern Mediterranean region by 4·0 additional months (mean 1·7, SE 0·2). Increases in the climatic suitability of both diseases will be greater in rural areas than in urban areas. The epidemic belt for both diseases will expand towards temperate areas. The population at risk of both diseases might increase by up to 4·7 additional billion people by 2070 relative to 1970-99, particularly in lowlands and urban areas. INTERPRETATION: Rising global mean temperature will increase the climatic suitability of both diseases particularly in already endemic areas. The predicted expansion towards higher altitudes and temperate regions suggests that outbreaks can occur in areas where people might be immunologically naive and public health systems unprepared. The population at risk of malaria and dengue will be higher in densely populated urban areas in the WHO African region, South-East Asia region, and the region of the Americas, although we did not account for urban-heat island effects, which can further alter the risk of disease transmission. FUNDING: UK Space Agency, Royal Society, UK National Institute for Health Research, and Swedish Research Council

Apparent Temperature and Cause-Specific Emergency Hospital Admissions in Greater Copenhagen, Denmark

One of the key climate change factors, temperature, has potentially grave implications for human health. We report the first attempt to investigate the association between the daily 3-hour maximum apparent temperature (Tappmax) and respiratory (RD), cardiovascular (CVD), and cerebrovascular (CBD) emergency hospital admissions in Copenhagen, controlling for air pollution. The study period covered 1 January 2002−31 December 2006, stratified in warm and cold periods. A case-crossover design was applied. Susceptibility (effect modification) by age, sex, and socio-economic status was investigated. For an IQR (8°C) increase in the 5-day cumulative average of Tappmax, a 7% (95% CI: 1%, 13%) increase in the RD admission rate was observed in the warm period whereas an inverse association was found with CVD (−8%, 95% CI: −13%, −4%), and none with CBD. There was no association between the 5-day cumulative average of Tappmax during the cold period and any of the cause-specific admissions, except in some susceptible groups: a negative association for RD in the oldest age group and a positive association for CVD in men and the second highest SES group. In conclusion, an increase in Tappmax is associated with a slight increase in RD and decrease in CVD admissions during the warmer months

Apparent Temperature and Air Pollution vs. Elderly Population Mortality in Metro Vancouver

Background: Meteorological conditions and air pollution in urban environments have been associated with general population and elderly mortality, showing seasonal variation. Objectives: This study is designed to evaluate the relationship between apparent temperature (AT) and air pollution (PM2.5) vs. mortality in elderly population of Metro Vancouver. Methods: Statistical analyses are performed on moving sum daily mortality rates vs. moving average AT and PM 2.5 in 1-, 2-, 3-, 5-, and 7-day models for all seasons, warm temperatures above 15uC, and cold temperatures below 10uC. Results: Approximately 37 % of the variation in all-season mortality from circulatory and respiratory causes can be explained by the variation in 7-day moving average apparent temperature (r 2 = 0.37, p,0.001). Although the analytical results from air pollution models show increasingly better prediction ability of longer time-intervals (r 2 = 0.012, p,0.001 in a 7-day model), a very weak negative association between elderly mortality and air pollution is observed. Conclusions: Apparent temperature is associated with mortality from respiratory and circulatory causes in elderly population of Metro Vancouver. In a changing climate, one may anticipate to observe potential health impacts from the projected high- and particularly from the low-temperature extremes