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Are health warnings for your country, region or city missing?

Health warnings are only issued for those countries and regions for which we have health data to estimate epidemiological associations (find details below). We express our complete willingness to add more countries, smaller regions, or even cities, either from Europe or other continents, if health data is kindly provided to us. If you have health data to share with us, please click here to send us an email.

Summary

Every day, we download and process the latest available temperature observations, as well as the new set of temperature forecasts for the next 15 days. We use the observations to correct the inherent errors of the forecasts (“bias-correction”), so that temperatures and their health effects are predicted as accurately and as far in advance as possible.

We used temperature observations and mortality records in each region to estimate the relation between these variables, which is different in each location. These so-called “epidemiological associations” quantify the actual risk of death at any given temperature and location based on real data. In Europe, the risk of death due to ambient temperatures is generally minimum twice every year: in late spring or early summer, and in late summer or early autumn. In general, the risk of death increases with increasing temperatures in summer (“heat warnings”), and with decreasing temperatures in autumn, winter and spring (“cold warnings”). We used these epidemiological associations to transform the temperature forecast for a given location and forecast date into 5 warning categories: a baseline warning state (“none”), when the risk of death is minimum, and 4 categories of heat and cold warnings (“low”, “moderate”, “high” and “extreme”), corresponding to increasing levels of risk of death.

A key aspect of the system is that, in each location, we estimated separate epidemiological associations for each sex and age group. These sex-specific and age-specific epidemiological associations were exclusively estimated with mortality records of the respective sex or age group, and therefore, they quantify the actual risk of death of the population subgroup at any given temperature and location based on real data. This means that we issue independent health warnings for each population subgroup based on the temperature forecasts and its corresponding sex-specific or age-specific epidemiological association. In general, the risk of death from heat is higher in women than in men, and consequently, heat warnings in summer are expected to be more frequent and of higher category in women than in men. Similarly, the risk of death from both heat and cold increases with age, and therefore, heat and cold warnings are expected to be more frequent and of higher category in the elderly all year round.

We here analysed how far in advance we can reliably forecast temperatures and their health effects. Temperatures can generally be forecast with some degree of confidence up to 15 days in advance in winter, and up to 14 days ahead in summer. Our capacity to predict the risk of death with some degree of confidence is somewhat smaller, up to 15 days in advance in winter, but only up to 11 days ahead in summer. We must however strongly emphasise that the reliability of the temperature forecasts and health warnings decreases as we predict more distant dates, with the best forecasts and warnings a few days ahead only. We generally recommend being cautious with temperature forecasts and related health warnings issued more than 7 days in advance.


Author contributions

Joan Ballester Claramunt: original idea, conceptualisation, overall methodology design, project funding, team creation/coordination, temperature/mortality epidemiological modelling, website descriptions, supervision of all steps.
Mireia Beas-Moix: project management, mortality data acquisition, website licensing.
Nadia Beltrán-Barrón: mortality data processing, website creation/design.
Raúl Fernando Méndez Turrubiates: temperature data processing, temperature population weighting.
Fabien Peyrusse: temperature data processing, temperature population weighting.
Marcos Quijal-Zamorano: temperature/mortality epidemiological modelling, temperature/mortality predictability assessment, temperature bias-correction.


Ballester J, Beas-Moix M, Beltrán-Barrón N, Méndez Turrubiates RF, Peyrusse F, Quijal-Zamorano M. Forecaster.Health. Available at https://forecaster.health/ (2024).

Mortality records

We used the spatiotemporally-homogeneous daily regional mortality database of the project EARLY-ADAPT. As of September 2024, the database contains over 164 million counts of deaths from 654 contiguous NUTS regions in 32 European countries, representing their entire urban and rural population of over 541 million people.


Temperature observations and forecasts

Every day, we obtain the most recent available hourly gridded (0.1° x 0.1°) 2-meter temperatures from the ERA5-Land reanalysis, here considered as a proxy for observations. We also obtain gridded (0.25° x 0.25°) 2-meter temperature forecasts issued at 00 UTC from ECMWF. Forecasts include 51 ensemble members with data every 3 hours at hourly lead times 0 to 144 (i.e. days 1 to 6), and every 6 hours from hourly lead times 144 to 354 (i.e. days 7 to 15). We compute the daily regional temperature observations and forecasts by weighting gridded temperatures with gridded population data for year 2018 from GISCO.

Then, we post-process the ensemble of temperature forecasts to bias-correct them against the temperature observations used in the epidemiological models. We apply a bias-correction method considering the most recent N = 30 pairs of observations and forecasts with respect to each forecast start date (BC-30). Thus, for any given region \(r\), observation date or forecast start date \(s\), and forecast lead time \(l\) (expressed in days), we calculate the correction \(c\) of the forecast ensemble members as

$$ c(r,s,l)=\frac{1}{N} \sum_{n=1}^{N}o(r,s-n)-f(r,s-n-l\mathit{+1},l) , $$

where \(o(r,s-n)\) and \(f(r,s-n-l,l)\) are the pairs of temperature observations and ensemble mean forecasts for all cases in the training dataset, respectively. We then add this correction individually to each of the forecast ensemble members to obtain the ensemble of bias-corrected temperature forecasts.


We used a time-series quasi-Poisson regression model in each region to derive estimates of region-specific temperature-lag-mortality risks with data from the period 2000-2019, following the methodology described here and here. The equation is as follows

$$ log(E(mort))=intercept+S(\textit{time, 8 df per year} )+dow+cb, $$

where \(mort\) denotes the daily time series of mortality counts; \(E\) corresponds to its expected value; \(S\) is a natural cubic spline of time with 8 degrees of freedom per year to adjust for the seasonal and long-term trends; \(dow\) corresponds to a categorical variable to control for the day of the week; and \(cb\) is the cross-basis function produced by a distributed lag non-linear model combining the exposure-response and lag-response associations. The exposure-response association was modelled with a natural cubic spline, with three internal knots placed at the 10th, 75th and 90th percentiles of the observed distribution of daily regional temperatures. The lag-response association was modelled with a natural cubic spline, with an intercept and three internal knots placed at equally-spaced intervals in the logarithmic scale, with lags ranging between 0 and 21 days. We then performed a multivariate multilevel meta-analysis, modelling dependencies of regions within countries through structured random effects, and including the location-specific temperature average and interquartile range as meta-predictors. The fitted meta-analytical model was used to derive the best linear unbiased predictions of the cumulative temperature-mortality association in each region, from which we estimated the regional minimum mortality temperature.

Every day, we transform the temperature observations and bias-corrected temperature forecasts into temperature related mortality (TRM) estimates and predictions, respectively. TRM represents the fraction of deaths attributable to non-optimal temperatures, calculated as

$$ TRM(d)=1-\frac{1}{RR(T(d))}, $$

where \(RR(T(d))\) is the relative risk at temperature \(T(d)\) of a given observed or forecast date \(d\). The relative risk is computed from the respective regional cumulative temperature-mortality association, centred at its minimum mortality temperature. We created five warning categories, for temperature related mortality values smaller than 5% (“none”), between 5% and 10% (“low”), between 10% and 15% (“moderate”), between 15% and 20% (“high”) and higher than 20% (“extreme”). “Cold” and “heat” warnings correspond to days with temperatures colder or warmer than the respective regional minimum mortality temperature, respectively.