The association between air temperature and covid-19 incidence is unclear, particularly regarding lag effects. Here we address this research gap using high resolution data from Italy. We obtained daily covid-19 cases, populations at risk, and mean daily air temperature from 97 Italian cities for the period 24 February through 21 September 2020. We fitted a mixed-effects distributed lag non-linear model, presenting the effects as relative risks (RR) and cumulative relative risks (RRcum).Negative increments in mean daily temperature produced approximately inverted U- shaped lag-responses, though for large positive increments in temperature, the peak RR occurred at the maximal lag of 14 days. The temperature exposure response curves generally showed an increased RR with increasing temperature, though the shape varied according to the lag period. Positive and negative increments in temperature caused increases and decreases in the RRcum respectively, though the plateau effect for negative increments was not observed above small positive increments in temperature.We postulate that latent variables correlated with temperature, such as frequency and duration of social activities, are the underlying cause of our observed trends. Nonetheless, our statistical model can be utilised to forecast cumulative covid-19 incidence rates assuming specified air temperature increments at the city level.

The exposure-lag response of air temperature on COVID-19 incidence is unclear and there have been concerns regarding the robustness of previous studies. Here we present an analysis of high spatial and temporal resolution using the distributed lag non-linear modelling (DLNM) framework. We first fit statistical models to select Italian cities, accounting for lag effects up to 10 days and several categories of potential confounders (policy, mobility, meteorological, and pollution variables). Estimates from these models are then synthesised using random effects meta-analysis to yield pooled estimates of the exposure-lag response presented as the relative risk (RR) and cumulative RR (RRcum). Though there was variation in the lag-specific exposure-response curves, the cumulative exposure response was approximately bell shaped, with highest risk at 19.8 °C, 2.39 [95% CI: 1.13; 2.94] times that at 4.7 °C which represented the lowest risk. Our work is in agreement with studies suggesting “lower” and “higher” temperatures might reduce covid-19 transmission, though our results suggest the optimum temperature for outdoor transmission might be higher than previously thought. Due to this uncertainty, our work underscores the need for facemasks and social distancing even in warm temperatures.