Challenges in developing methods for quantifying the effects of weather and climate on water-associated diseases: A systematic review

Infectious diseases attributable to unsafe water supply, sanitation and hygiene (e.g. Cholera, Leptospirosis, Giardiasis) remain an important cause of morbidity and mortality, especially in low-income countries. Climate and weather factors are known to affect the transmission and distribution of infectious diseases and statistical and mathematical modelling are continuously developing to investigate the impact of weather and climate on water-associated diseases. There have been little critical analyses of the methodological approaches. Our objective is to review and summarize statistical and modelling methods used to investigate the effects of weather and climate on infectious diseases associated with water, in order to identify limitations and knowledge gaps in developing of new methods. We conducted a systematic review of English-language papers published from 2000 to 2015. Search terms included concepts related to water-associated diseases, weather and climate, statistical, epidemiological and modelling methods. We found 102 full text papers that met our criteria and were included in the analysis. The most commonly used methods were grouped in two clusters: process-based models (PBM) and time series and spatial epidemiology (TS-SE). In general, PBM methods were employed when the bio-physical mechanism of the pathogen under study was relatively well known (e.g. Vibrio cholerae); TS-SE tended to be used when the specific environmental mechanisms were unclear (e.g. Campylobacter). Important data and methodological challenges emerged, with implications for surveillance and control of water-associated infections. The most common limitations comprised: non-inclusion of key factors (e.g. biological mechanism, demographic heterogeneity, human behavior), reporting bias, poor data quality, and collinearity in exposures. Furthermore, the methods often did not distinguish among the multiple sources of time-lags (e.g. patient physiology, reporting bias, healthcare access) between environmental drivers/exposures and disease detection. Key areas of future research include: disentangling the complex effects of weather/climate on each exposure-health outcome pathway (e.g. person-to-person vs environment-to-person), and linking weather data to individual cases longitudinally

Performance of wild-serbian ganoderma lucidum mycelium in treating synthetic sewage loading using batch bioreactor

The fluctuation of domestic wastewater characteristic inhibits the current conventional microbial-based treatment. The bioremediation fungi has received attention and reported to be an effective alternative to treat industrial wastewater. Similar efficient performance is envisaged for domestic wastewater whereby assessed performance of fungi for varying carbon-to-nitrogen ratios in domestic wastewater is crucial. Thus, the performance of pre-grown wild-Serbian Ganoderma lucidum mycelial pellets (GLMPs) was evaluated on four different synthetic domestic wastewaters under different conditions of initial pH (pH 4, 5, and 7) and chemical oxygen demand (COD) to nitrogen (COD/N) ratio of 3.6:1, 7.1:1, 14.2:1, and 17.8:1 (C3.6N1, C7.1N1, C14.2N1, and C17.8N1). The COD/N ratios with a constant concentration of ammonia–nitrogen (NH3–N) were chosen on the basis of the urban domestic wastewater characteristics sampled at the inlet basin of a sewage treatment plant (STP). The parameters of pH, COD, and NH3–N were measured periodically during the experiment. The wild-Serbian GLMPs efficiently removed the pollutants from the synthetic sewage. The COD/N ratio of C17.8N1 wastewater had the best COD and NH3–N removal, as compared to the lower COD/N ratio, and the shortest treatment time was obtained in an acidic environment at pH 4. The highest percentage for COD and NH3–N removal achieved was 96.0% and 93.2%, respectively. The results proved that the mycelium of GLMP has high potential in treating domestic wastewater, particularly at high organic content as a naturally sustainable bioremediation system