# Developing a Heatwave Early Warning System for Sweden: Evaluating Sensitivity of Different Epidemiological Modelling Approaches to Forecast Temperatures

^{1}

^{2}

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^{†}

## Abstract

**:**

## 1. Introduction

## 2. Experimental Section

#### 2.1. Data

^{2}in the period 1998–2005 and 25 × 25 km

^{2}in 2006–2007. Higher resolution forecast models have been in use in Sweden for part of the time period studied. The ECMWF data was chosen because of consistency for the time period studied. Also for the longer forecast lead times (beyond 2 days) ECMWF was the best source of information for the studied time period.

#### 2.2. Study Design

_{t}):

_{t}it the day of the week, S(trend

_{t}) is a smooth function for the time trend over the entire period and a function of temperature depending on the underlying statistical model.

## 3. Results and Discussion

#### 3.1. Forecasts

Forecast Length | Intercept | Slope |
---|---|---|

1-day forecast | 2.283 | 0.829 |

2-day forecast | 2.388 | 0.825 |

3-day forecast | 2.753 | 0.795 |

**Figure 1.**Observed vs. 1-day forecast temperatures, Stockholm, 1998–2007. Red line shows the linear estimate for an unbiased fit and the blue the estimated linear relationship between the variables.

#### 3.2. Risk Models

^{2}. At these temperatures the threshold model found statistically significant increases in the daily number of deaths of 3.1, 10 and 20.5%. These estimated increases correspond to the temperature ranges 20–27 °C, 27–30 °C and above 30 °C. The reason to only include the two higher threshold values in the Swedish HEWS is explained by the low risk increase estimated for the lowest threshold. Overall, this model explains 17.6% of the variation in daily mortality.

**Figure 2.**Comparison of which models identify heatwave days as risk days. Each grey area represents a period where risk days were identified. The markers describe which models classified each day as a risk day. The date on the x-axis describes the first day of each period with elevated mortality risk.

**Table 2.**Sensitivity scores for the four models using forecast and adjusted forecast temperatures, and different issuing time frames.

Model Type | Forecast | Adjusted Forecast | ||||
---|---|---|---|---|---|---|

1-day | 2-day | 3-day | 1-day | 2-day | 3-day | |

DLNM | 0.62 | 0.05 | NA | 0.67 | 0.10 | NA |

GAM | 0.78 | 0.53 | 0.16 | 0.86 | 0.72 | 0.41 |

PWL | 0.79 | 0.56 | 0.19 | 0.88 | 0.72 | 0.47 |

THR | 0.70 | 0.30 | 0.09 | 0.84 | 0.57 | 0.23 |

**Table 3.**Positive prediction values for the four models using forecast and adjust forecasted temperatures, and different issuing time frames.

Model Type | Forecast | Adjusted Forecast | ||||
---|---|---|---|---|---|---|

1-day | 2-day | 3-day | 1-day | 2-day | 3-day | |

DLNM | 1.00 | 1.00 | NA | 0.88 | 0.50 | NA |

GAM | 1.00 | 0.98 | 0.94 | 0.99 | 0.94 | 0.93 |

PWL | 1.00 | 1.00 | 1.00 | 0.98 | 0.97 | 0.93 |

THR | 0.96 | 0.95 | 1.00 | 0.97 | 0.91 | 0.89 |

**Figure 3.**Risk estimates for the four models using fitted values from observed temperatures and the forecast temperatures on days with temperature above 26 °C. On the x-axis is the estimated risk increases in percent produced by observed temperatures and on the y-axis the estimated risk increase produced by different forecast times for each day in the study period for the: (

**a**) DLNM model; (

**b**) GAM model; (

**c**) PWL model; (

**d**) THR model.

## 4. Discussion

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**MDPI and ACS Style**

Åström, C.; Ebi, K.L.; Langner, J.; Forsberg, B.
Developing a Heatwave Early Warning System for Sweden: Evaluating Sensitivity of Different Epidemiological Modelling Approaches to Forecast Temperatures. *Int. J. Environ. Res. Public Health* **2015**, *12*, 254-267.
https://doi.org/10.3390/ijerph120100254

**AMA Style**

Åström C, Ebi KL, Langner J, Forsberg B.
Developing a Heatwave Early Warning System for Sweden: Evaluating Sensitivity of Different Epidemiological Modelling Approaches to Forecast Temperatures. *International Journal of Environmental Research and Public Health*. 2015; 12(1):254-267.
https://doi.org/10.3390/ijerph120100254

**Chicago/Turabian Style**

Åström, Christofer, Kristie L. Ebi, Joakim Langner, and Bertil Forsberg.
2015. "Developing a Heatwave Early Warning System for Sweden: Evaluating Sensitivity of Different Epidemiological Modelling Approaches to Forecast Temperatures" *International Journal of Environmental Research and Public Health* 12, no. 1: 254-267.
https://doi.org/10.3390/ijerph120100254