Changing trends in foodborne disease are influenced by many factors, including temperature. Globally and in Australia, warmer ambient temperatures are projected to rise if climate change continues. Salmonella spp. are a temperature-sensitive pathogen and rising temperature can have a substantial effect on disease burden affecting human health. We examined the relationship between temperature and Salmonella spp. and serotype notifications in Adelaide, Australia. Time-series Poisson regression models were fit to estimate the effect of temperature during warmer months on Salmonella spp. and serotype cases notified from 1990 to 2012. Long-term trends, seasonality, autocorrelation and lagged effects were included in the statistical models. Daily Salmonella spp. counts increased by 1·3% [incidence rate ratio (IRR) 1·013, 95% confidence interval (CI) 1·008–1·019] per 1 °C rise in temperature in the warm season with greater increases observed in specific serotype and phage-type cases ranging from 3·4% (IRR 1·034, 95% CI 1·008–1·061) to 4·4% (IRR 1·044, 95% CI 1·024–1·064). We observed increased cases of S. Typhimurium PT9 and S. Typhimurium PT108 notifications above a threshold of 39 °C. This study has identified the impact of warm season temperature on different Salmonella spp. strains and confirms higher temperature has a greater effect on phage-type notifications. The findings will contribute targeted information for public health policy interventions, including food safety programmes during warmer weather.

We studied associations between monthly counts of laboratory-confirmed cases of salmonellosis, ambient air temperature and precipitation in four settings in Kazakhstan. We observed a linear association between the number of cases of salmonellosis and mean monthly temperature during the same months only in Astana: an increase of 1°C was associated with a 5·5% [95% confidence interval (CI) 2·2–8·8] increase in the number of cases. A similar association, although not reaching the level of significance was observed in the Southern Kazakhstan region (3·5%, 95% CI −2·1 to 9·1). Positive association with precipitation with lag 2 was found in Astana: an increase of 1 mm was associated with a 0·5% (95% CI 0·1–1·0) increase in the number of cases. A similar association, but with lag 0 was observed in Southern Kazakhstan region (0·6%, 95% CI 0·1–1·1). The results may have implications for the future patterns of salmonellosis in Kazakhstan with regard to climate change.

Mosquito-borne Sindbis virus (SINV) causes rash-arthritis syndrome in Finland. Major outbreaks with approximately 7-year cycles have caused substantial burden of illness. Forest dwelling grouse are suspected to be amplifying hosts, with the infection transmitted to humans by mosquito bites. SINV infection surveillance data for 1984–2010 were used to create a negative binomial hurdle model, with seasonality, long-term cycles, climatic, ecological and socioeconomic variables. Climatic factors during early summer and amount of snow in April described the occurrence and incidence of SINV infections. Regulated water shore and hatch-year black grouse density described the occurrence, while population working in agriculture, agricultural land (negative) and income (negative) described the incidence of the disease. The prediction for 2009 was 85 cases (95% prediction interval 2-1187), while the actual occurrence was 106. We identified novel and known risk factors. The prevention of SINV infections in regulated water areas by infected mosquito populations should be targeted.

Rotavirus is a common viral cause of severe diarrhoea. For the underlying cause of rotavirus seasonality, the meteorological factor has been suspected, whereas quantitative correlation between seasonality and meteorological factor has not been fully investigated. In this study, we investigated the correlation of temporal patterns of the isolation rate of rotavirus with meteorological condition (temperature, relative humidity, rainfall) in Kolkata, India. We used time-series analysis combined with spectral analysis and least squares method. A 1-year cycle explained underlying variations of rotavirus and meteorological data. The 1-year cycle for rotavirus data was correlated with an opposite phase to that for meteorological data. Relatively high temperature could be associated with a low value of isolation rate of rotavirus in the monsoon season. Quantifying a correlation of rotavirus infections with meteorological conditions might prove useful in predicting rotavirus epidemics and health services could plan accordingly.

Global climate change is expected to affect the frequency, intensity and duration of extreme water-related weather events such as excessive precipitation, floods, and drought. We conducted a systematic review to examine waterborne outbreaks following such events and explored their distribution between the different types of extreme water-related weather events. Four medical and meteorological databases (Medline, Embase, GeoRef, PubMed) and a global electronic reporting system (ProMED) were searched, from 1910 to 2010. Eighty-seven waterborne outbreaks involving extreme water-related weather events were identified and included, alongside 235 ProMED reports. Heavy rainfall and flooding were the most common events preceding outbreaks associated with extreme weather and were reported in 55·2% and 52·9% of accounts, respectively. The most common pathogens reported in these outbreaks were Vibrio spp. (21·6%) and Leptospira spp. (12·7%). Outbreaks following extreme water-related weather events were often the result of contamination of the drinking-water supply (53·7%). Differences in reporting of outbreaks were seen between the scientific literature and ProMED. Extreme water-related weather events represent a risk to public health in both developed and developing countries, but impact will be disproportionate and likely to compound existing health disparities.

Associations between monthly counts of all laboratory-confirmed cases of salmonellosis in Arkhangelsk, northern Russia, from 1992 to 2008 and climatic variables with lags 0–2 were studied by three different models. We observed a linear association between the number of cases of salmonellosis and mean monthly temperature with a lag of 1 month across the whole range of temperatures. An increase of 1 °C was associated with a 2·04% [95% confidence interval (CI) 0·25–3·84], 1·84% (95% CI 0·06–3·63) and 2·32% (95% CI 0·38–4·27) increase in different models. Only one of the three models suggested an increase in the number of cases, by 0·24% (95% CI 0·02–0·46) with an increase in precipitation by 1 mm in the same month. Higher temperatures were associated with higher monthly counts of salmonellosis while the association with precipitation was less certain. The results may have implications for the future patterns of enteric infections in northern areas related to climate change.

Studies of temporal and spatial patterns of diarrhoeal disease can suggest putative aetiological agents and environmental or socioeconomic drivers. Here, the seasonal patterns of monthly acute diarrhoeal morbidity in Thailand, where diarrhoeal morbidity is increasing, are explored. Climatic data (2003–2006) and Thai Ministry of Health annual reports (2003–2009) were used to construct a spatially weighted panel regression model. Seasonal patterns of diarrhoeal disease were generally bimodal with aetiological agents peaking at different times of the year. There is a strong association between daily mean temperature and precipitation and the incidence of hospitalization due to acute diarrhoea in Thailand leading to a distinct spatial pattern in the seasonal pattern of diarrhoea. Model performance varied across the country in relation to per capita GDP and population density. While climatic factors are likely to drive the general pattern of diarrhoeal disease in Thailand, the seasonality of diarrhoeal disease is dampened in affluent urban populations.

Japanese weather data for areas that produced Campylobacter spp.-positive chicken products were compared with those for areas producing negative samples. Regarding samples produced during the period of rising temperature (spring and summer), the mean weekly air temperatures for Campylobacter-positive samples were higher than those for negative samples for the period of the week in which the samples were purchased (18·7°C vs. 13·1°C, P=0·006) to a 12-week lag (12 weeks before purchasing samples; 7·9°C vs. 3·4°C, P=0·009). Significant differences in weekly mean minimum humidity and sunshine duration per day were also observed for 1- and 2-week lag periods. We postulated that the high air temperature, high humidity and short duration of sunshine for the chicken-rearing period increased Campylobacter colonization in chickens during the period of rising temperature. Consequently, the number of Campylobacter-contaminated chicken products on the market in Japan may fluctuate because of the climatic conditions to which reared chickens are exposed.

Expert opinion was elicited to undertake a qualitative risk assessment to estimate the current and future risks to the European Union (EU) from five vector-borne viruses listed by the World Organization for Animal Health. It was predicted that climate change will increase the risk of incursions of African horse sickness virus (AHSV), Crimean-Congo haemorrhagic fever virus (CCHFV) and Rift Valley fever virus (RVFV) into the EU from other parts of the world, with African swine fever virus (ASFV) and West Nile virus (WNV) being less affected. Currently the predicted risks of incursion were lowest for RVFV and highest for ASFV. Risks of incursion were considered for six routes of entry (namely vectors, livestock, meat products, wildlife, pets and people). Climate change was predicted to increase the risk of incursion from entry of vectors for all five viruses to some degree, the strongest effects being predicted for AHSV, CCHFV and WNV. This work will facilitate identification of appropriate risk management options in relation to adaptations to climate change.