From the Hong Kong Observatory [22] we retrieved daily meteorological observations including temperature (maximum, mean, minimum), mean dew point temperature, mean relative humidity, global solar radiation and total evaporation.
To obtain complete weather data for all 100 counties, the missing mean temperature and mean dew point temperature data were imputed by taking the average of the mean temperature and/or mean dew point temperature of all counties that bordered the county that was missing data.
Singapore had the highest 101-week mean temperature, with standard deviation (28.3 ± 0.94°C) and mean dew point (23.8 ± 0.81°C), while those items were lowest in Japan, with 15.1 ± 8.79°C and 8.85 ± 9.28°C, respectively.
Models also controlled for meteorology (using data from the National Climatic Data Center for St . Louis Lambert International Airport including daily maximum temperature (lag 0) using indicator variables for each °C; daily minimum temperature (1 4 day moving average) using cubic terms; and mean dew point (0 2 day moving average) using cubic terms.
The daily mean temperature (degrees Celsius, °C), mean dew point (degrees Celsius, °C), mean visibility (kilometer, km), mean wind speed (meters per second, m/s), and precipitation amount (millimeters, mm) were calculated as the average values for the corresponding weeks in each area.
We modeled each pollutant assuming a linear relationship and controlled for same day (lag 0) ambient temperature assuming a quadratic relationship, and mean ambient temperature over the past 3 days (lags 1 3), same day dew point, and mean dew point over the past 3 days assuming linear relationships with the outcome.
Climate normals for 1981 2010 for average annual precipitation, average minimum January temperature, and average maximum July temperature, plus the mean dew-point temperature for 2010 were obtained from PRISM data (PRISM Climate Group at Oregon State University 2012).
The values dew0 and dew1 3 are defined similarly for the 24-hr mean dew-point temperature, and DOW t is a set of indicator variables for the day of the week.
For each model, we focused on reporting the estimated effect of an air pollution variable, which was adjusted with the three meteorological factors (daily mean temperature, dew point temperature and wind speed).
The primary outcome measures were the incidence rate ratio of disease occurrence associated with mean weekly maximum temperature, cumulative weekly temperature, mean weekly dew point temperature, cumulative weekly precipitation, and the presence of ≥ 1 day of heavy rainfall (≥ 50 mm) during the month prior to symptom onset.
In a model controlling for mean weekly maximum temperature and cumulative weekly precipitation, mean weekly dew point temperature was significantly associated with the incidence of reported WNV infection 1 3 weeks later, but not during the same week.
