Determining meteorologically-favorable zones for seasonal influenza activity in Hong Kong.

Investigations of simple and accurate meteorology classification systems for influenza epidemics, particularly in subtropical regions, are limited. To assist in preparing for potential upsurges in the demand on healthcare facilities during influenza seasons, our study aims to develop a set of meteorologically-favorable zones for epidemics of influenza A and B, defined as the intervals of meteorological variables with prediction performance optimized. We collected weekly detection rates of laboratory-confirmed influenza cases from four local major hospitals in Hong Kong between 2004 and 2019. Meteorological and air quality records for hospitals were collected from their closest monitoring stations. We employed classification and regression trees to identify zones that optimize the prediction performance of meteorological data in influenza epidemics, defined as a weekly rate > 50th percentile over a year. According to the results, a combination of temperature > 25.1℃ and relative humidity > 79% was favorable to epidemics in hot seasons, whereas either temperature < 16.4℃ or a combination of < 20.4℃ and relative humidity > 76% was favorable to epidemics in cold seasons. The area under the receiver operating characteristic curve (AUC) in model training achieved 0.80 (95% confidence interval [CI], 0.76-0.83) and was kept at 0.71 (95%CI, 0.65-0.77) in validation. The meteorologically-favorable zones for predicting influenza A or A and B epidemics together were similar, but the AUC for predicting influenza B epidemics was comparatively lower. In conclusion, we established meteorologically-favorable zones for influenza A and B epidemics with a satisfactory prediction performance, even though the influenza seasonality in this subtropical setting was weak and type-specific.

Association between meteorological variations and activities of influenza A and B across different climate zones: a multi-region modelling analysis across the globe.

OBJECTIVE: To elucidate the effects of meteorological variations on the activity of influenza A and B in 11 sites across different climate regions. METHODS: Daily numbers of laboratory-confirmed influenza A and B cases from 2011-2015 were collected from study sites where the corresponding daily mean temperature, relative humidity, wind speed and daily precipitation amount were used for boosted regression trees analysis on the marginal associations and the interaction effects. RESULTS: Cold temperature was a major determinant that favored both influenza A and B in temperate and subtropical sites. Temperature-to-influenza A, but not influenza B, exhibited a U-shape association in subtropical and tropical sites. High relative humidity was also associated with influenza activities but was less consistent with influenza B activity. Compared with relative humidity, absolute humidity had a stronger association - it was negatively associated with influenza B activity in temperate zones, but was positively associated with both influenza A and B in subtropical and tropical zones. CONCLUSION: The association between meteorological factors and with influenza activity is virus type specific and climate dependent. The heavy influence of temperature on influenza activity across climate zones implies that global warming is likely to have an impact on the influenza burden.

An increasing trend of rural infections of human influenza A (H7N9) from 2013 to 2017: A retrospective analysis of patient exposure histories in Zhejiang province, China.

BACKGROUND: Although investigations have shown that closing live poultry markets (LPMs) is highly effective in controlling human influenza A (H7N9) infections, many of the urban LPMs were shut down, but rural LPMs remained open. This study aimed to compare the proportional changes between urban and rural infections in the Zhejiang province from 2013 to 2017 by analyzing the exposure histories of human cases. METHODS: All laboratory-confirmed cases of H7N9 from 2013 (the first wave) to 2017 (the fifth wave) in the Zhejiang province of China were analyzed. Urban and rural infections were defined based on the locations of poultry exposure (direct and indirect) in urban areas (central towns) and rural areas (towns and villages on the outskirts of cities). A Chi-square trend test was used to compare the proportional trend between urban and rural infections over time and logistic regression was used to obtain the odds ratio by years. RESULTS: From 2013 to 2017, a statistically significant trend in rural infections was observed (p <0.01). The incremental odds ratio by years of rural infections was 1.59 with 95% confidence intervals of 1.34 to 1.86. Each year, significant increases in the proportion of live poultry transactions in LPMS and poultry processing plants were detected in conjunction with an increased proportion of urban and rural infections. CONCLUSION: The empirical evidence indicated a need for heightened infection control measures in rural areas, such as serving rural farms and backyards as active surveillance points for the H7N9 virus. Other potential interventions such as the vaccination of poultry and extending the closure of LPMs to the provincial level require further careful investigations.