Hantaviruses are endemic in the Balkans, particularly in Serbia, where sporadic cases and/or outbreaks of hantaviral human disease have been reported repeatedly, and evidenced serologically. Here, we present genetic detection of Dobrava-Belgrade virus (DOBV) hantaviral sequences in wild rodents trapped in central Serbia. All the animals were pre-screened serologically by indirect immunofluorescence (IF) test and only those with a positive finding of hantaviral antigens were further tested by polymerase chain reaction. Of the total of 104 trapped animals, 20 were found to be IF positive and of those three were positive for hantaviral RNA: one Microtus arvalis for Tula virus, and one each of Apodemus agrarius and Glis glis for DOBV. Phylogenetic analysis of the obtained sequences implies putative DOBV spillover infection of A. agrarius and G. glis from Apodemus flavicollis. However, future investigations should help to identify the most common natural host and geographical distribution of DOBV in its reservoir hosts in Serbia.

The transmission of haemorrhagic fever with renal syndrome (HFRS) is deeply influenced by the reservoir and hantavirus prevalence rate. In this study, a surveillance on human HFRS cases, relative rodent abundance, and hantavirus infection prevalence was conducted in Shaanxi province, China, during 1984–2012. A generalized linear model with Poisson-distributed residuals and a log link was used to quantify the relationship between reservoir, virus and HFRS cases. The result indicated that there was a significant association of HFRS incidence with relative rodent density and the prevalence rate. This research provides evidence that the changes of infection prevalence in the reservoir could lead directly to the emergence of a new epidemic. It was concluded that the measurement of a number of these variables could be used in disease surveillance to give useful advance warning of potential disease epidemics.

Dengue is regarded as the most important arboviral disease. Although sporadic cases have been reported, serotypes responsible for outbreaks have not been identified from central India over the last 20 years. We investigated two outbreaks of febrile illness, in August and November 2012, from Korea district (Chhattisgarh) and Narsinghpur district (Madhya Pradesh), respectively. Fever and entomological surveys were conducted in the affected regions. Molecular and serological tests were conducted on collected serum samples. Dengue-specific amplicons were sequenced and phylogenetic analyses were performed. In Korea and Narsinghpur districts 37·3% and 59% of cases were positive, respectively, for dengue infection, with adults being the worst affected. RT–PCR confirmed dengue virus serotype 1 genotype III as the aetiology. Ninety-six percent of infections were primary. This is the first time that dengue virus 1 outbreaks have been documented from central India. Introduction of the virus into the population and a conducive mosquitogenic environment favouring increased vector density caused the outbreak. Timely diagnosis and strengthening vector control measures are essential to avoid future outbreaks.

We evaluated laboratory reports as early indicators of West Nile virus (WNV) disease cases in Texas. We compared WNV laboratory results in the National Electronic Disease Surveillance System Base System (NBS) to WNV disease cases reported to the state health department from 2008 to 2012. We calculated sensitivity and positive predictive value (PPV) of NBS reports, estimated the number of disease cases expected per laboratory report, and determined lead and lag times. The sensitivity and PPV of NBS laboratory reports were 86% and 77%, respectively. For every 10 positive laboratory reports, we expect 9·0 (95% confidence interval 8·9–9·2) reported disease cases. Laboratory reports preceded case reports with a lead time of 7 days. Electronic laboratory reports provided longer lead times than manually entered reports (P < 0·01). NBS laboratory reports are useful estimates of future reported WNV disease cases and may provide timely information for planning public health interventions.

Influenza surveillance enables systematic collection of data on spatially and demographically heterogeneous epidemics. Different data collection mechanisms record different aspects of the underlying epidemic with varying bias and noise. We aimed to characterize key differences in weekly incidence data from three influenza surveillance systems in Melbourne, Australia, from 2009 to 2012: laboratory-confirmed influenza notified to the Victorian Department of Health, influenza-like illness (ILI) reported through the Victorian General Practice Sentinel Surveillance scheme, and ILI cases presenting to the Melbourne Medical Deputising Service. Using nonlinear regression, we found that after adjusting for the effects of geographical region and age group, characteristics of the epidemic curve (including season length, timing of peak incidence and constant baseline activity) varied across the systems. We conclude that unmeasured factors endogenous to each surveillance system cause differences in the disease patterns recorded. Future research, particularly data synthesis studies, could benefit from accounting for these differences.

Weather conditions and social contact patterns provide some clues to understanding year-round influenza epidemics in the tropics. Recent studies suggest that contact patterns may direct influenza transmission in the tropics as critically as the aerosol channel in temperate regions. To examine this argument, we analysed a representative nationwide survey dataset of contact diaries with comprehensive weather data in Taiwan. Methods we used included model-free estimated relative changes in reproduction number, R 0; relative changes in the number of contacts; and model-based estimated relative changes in mean contacts using zero-inflated negative binomial regression models. Overall, social contact patterns clearly differ by demographics (such as age groups), personal idiosyncrasies (such as personality and happiness), and social institutions (such as the division of weekdays and weekend days). Further, weather conditions also turn out to be closely linked to contact patterns under various circumstances. Fleeting contacts, for example, tend to diminish when it rains hard on weekdays, while physical contacts also decrease during weekend days with heavy rain. Frequent social contacts on weekdays and under good weather conditions, including high temperature and low absolute humidity, all might facilitate the transmission of infectious diseases in tropical regions.