RESUMEN
Compliance of drinking-water to bacteriological parameters serves as a surrogate measure of the risk of water-borne diseases. Understanding the risk of water-borne diseases could help promote healthy behaviors such as household water treatment and safe water storage practices and advocacy to increase access to centrally-managed piped water. The objective of this research was to assess the current status of compliance and to geospatially analyze the probability of compliance to bacteriological parameters in the Western Province of Sri Lanka. A drinking-water quality survey was conducted among 4508 households representing four water-source types: National Water Supply and Drainage Board (NWSDB), dug wells, Community Water Supply (CWS), and tube wells, and other sources. Besides, a detailed assessment of selected NWSDB and CWS supplies was done. Water samples were tested for the total coliform count, thermotolerant coliform count, and free residual chlorine levels against the Sri Lanka Standards. Indicator kriging was performed using the geospatial analyst tool of ArcGIS version 10.6 for different water source types to interpolate the probability of compliance for both total coliform count and thermotolerant coliform count. The bacteriological compliance decreased from NWSDB to tube wells and other sources to CWS to dug wells. The interpolation maps confirm the relatively higher compliance of NWSDB for bacteriological parameters compared to other sources. Areas with a high probability of compliance for both parameters show considerable overlap with urban areas with a supply of centrally managed water from the NWSDB. It is recommended to expand the coverage of NWSDB water, strengthen the drinking-water quality surveillance system and water safety plans, and promote household water treatment and safe storage practices in the Western Province of Sri Lanka.
Asunto(s)
Agua Potable , Agua Potable/análisis , Sri Lanka , Microbiología del Agua , Calidad del Agua , Abastecimiento de Agua , Pozos de AguaRESUMEN
In response to an outbreak of coronavirus disease 2019 (COVID-19) within a cluster of Navy personnel in Sri Lanka commencing from 22nd April 2020, an aggressive outbreak management program was launched by the Epidemiology Unit of the Ministry of Health. To predict the possible number of cases within the susceptible population under four social distancing scenarios, the COVID-19 Hospital Impact Model for Epidemics (CHIME) was used. With increasing social distancing, the epidemiological curve flattened, and its peak shifted to the right. The observed or actually reported number of cases was above the projected number of cases at the onset; however, subsequently, it fell below all predicted trends. Predictive modelling is a useful tool for the control of outbreaks such as COVID-19 in a closed community.