scholarly journals SARS-CoV-2: fate in water environments and sewage surveillance as an early warning system

2021 ◽  
Author(s):  
Deepak Panchal ◽  
Purusottam Tripathy ◽  
Om Prakash ◽  
Abhishek Sharma ◽  
Sukdeb Pal
Keyword(s):  
Wastewater Treatment ◽  
Early Warning ◽  
Disease Surveillance ◽  
Early Warning System ◽  
Warning System ◽  
Quality Criteria ◽  
Well Being ◽  
Coliform Bacteria ◽  
Surveillance Systems ◽  
Water Quality Criteria

Abstract Coronavirus disease has emerged as one of the greatest threats to human well-being. Currently, the whole world is fighting against this pandemic that transmit either through exposure to virus laden respiratory or water droplets or by touching the virus contaminated surfaces. The viral load in feces of an infected patient varies according to the severity of the disease. Subsequent detection of viral genome (SARS-COV-2) in human feces and sewage systems is an emerging concern for public health. This also dictates to reinforce the existing sewage/wastewater treatment facilities. Rapid monitoring is the key to prevent and control the current mass transmission. Wastewater-Based Epidemiology (WBE) is a potential epidemiology tool that can act as a complementary approach for current infectious disease surveillance systems and an early warning system for disease outbreaks. In a developing country like India, inadequate wastewater treatment systems, low-operational facility and relaxed surface water quality criteria even in terms of fecal coliform bacteria are the major challenges for WBE. Herein, we review the occurrence, transmission, survival of SARS-CoV-2, disinfection and potential of sewage surveillance as an early warning system for COVID-19 spread. We also discuss the challenges of open-defecation practices affecting sewage-surveillance in real-time in densely populated developing countries like India.

scholarly journals Assessment of electronic disease early warning system for improved disease surveillance and outbreak response in Yemen

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Fekri Dureab ◽  
Kamran Ahmed ◽  
Claudia Beiersmann ◽  
Claire J. Standley ◽  
Ali Alwaleedi ◽  
...  
Keyword(s):  
Public Health ◽  
Data Quality ◽  
Early Warning ◽  
Predictive Value ◽  
Disease Surveillance ◽  
Early Warning System ◽  
Warning System ◽  
Health Facilities ◽  
Public Health Response ◽  
Attributes Data

Abstract Background Diseases Surveillance is a continuous process of data collection, analysis interpretation and dissemination of information for swift public health action. Recent advances in health informatics have led to the implementation of electronic tools to facilitate such critical disease surveillance processes. This study aimed to assess the performance of the national electronic Disease Early Warning System in Yemen (eDEWS) using system attributes: data quality, timeliness, stability, simplicity, predictive value positive, sensitivity, acceptability, flexibility, and representativeness, based on the Centres for Disease Control & Prevention (US CDC) standard indicators. Methods We performed a mixed methods study that occurred in two stages: first, the quantitative data was collected from weekly epidemiological bulletins from 2013 to 2017, all alerts of 2016, and annual eDEWS reports, and then the qualitative method using in-depth interviews was carried out in a convergent strategy. The CDC guideline used to describe the following system attributes: data quality (reporting, and completeness), timeliness, stability, simplicity, predictive value positive, sensitivity, acceptability, flexibility and representativeness. Results The finding of this assessment showed that eDEWS is a resilient and reliable system, and despite the conflict in Yemen, the system is still functioning and expanding. The response timeliness remains a challenge, since only 21% of all eDEWS alerts were verified within the first 24 h of detection in 2016. However, identified gaps did not affect the system’s ability to identify outbreaks in the current fragile situation. Findings show that eDEWS data is representative, since it covers the entire country. Although, eDEWS covers only 37% of all health facilities, this represents 83% of all functional health facilities in all 23 governorates and all 333 districts. Conclusion The quality and timeliness of responses are major challenges to eDEWS’ functionality, the eDEWS remains the only system that provides regular data on communicable diseases in Yemen. In particular, public health response timeliness needs improvement.

scholarly journals Spatial and Temporal Trends of SARS-CoV-2 RNA from Wastewater Treatment Plants over 6 Weeks in Cape Town, South Africa

2021 ◽  
Vol 18 (22) ◽  
pp. 12085
Author(s):  
Renée Street ◽  
Angela Mathee ◽  
Noluxabiso Mangwana ◽  
Stephanie Dias ◽  
Jyoti Rajan Sharma ◽  
...  
Keyword(s):  
South Africa ◽  
Wastewater Treatment ◽  
Early Warning ◽  
Early Warning System ◽  
Wastewater Treatment Plants ◽  
Cape Town ◽  
Warning System ◽  
Western Cape ◽  
Western Cape Province ◽  
The City

Recent scientific trends have revealed that the collection and analysis of data on the occurrence and fate of SARS-CoV-2 in wastewater may serve as an early warning system for COVID-19. In South Africa, the first COVID-19 epicenter emerged in the Western Cape Province. The City of Cape Town, located in the Western Cape Province, has approximately 4 million inhabitants. This study reports on the monitoring of SARS-CoV-2 RNA in the wastewater of the City of Cape Town’s wastewater treatment plants (WWTPs) during the peak of the epidemic. During this period, the highest overall median viral RNA signal was observed in week 1 (9200 RNA copies/mL) and declined to 127 copies/mL in week 6. The overall decrease in the amount of detected viral SARS-CoV-2 RNA over the 6-week study period was associated with a declining number of newly identified COVID-19 cases in the city. The SARS-CoV-2 early warning system has now been established to detect future waves of COVID-19.

scholarly journals Implementing the Health Early Warning System based on syndromic and event-based surveillance at the 2019 Hajj

2020 ◽  
Vol 26 (12) ◽  
pp. 1570-1575
Author(s):  
Kingsley Lezor Bieh ◽  
Anas Khan ◽  
Saber Yezli ◽  
Ahmed El Ganainy ◽  
Sari Asiri ◽  
...  
Keyword(s):  
Early Warning ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Early Warning System ◽  
Situational Awareness ◽  
Warning System ◽  
Control Measures ◽  
Health Events ◽  
Disease Surveillance System ◽  
Event Based

Background: During the 2019 Hajj, the Ministry of Health in Saudi Arabia implemented for the first time a health early warning system for rapid detection and response to health threats. Aims: This study aimed to describe the early warning findings at the Hajj to highlight the pattern of health risks and the potential benefits of the disease surveillance system. Methods: Using syndromic surveillance and event-based surveillance data, the health early warning system generated automated alarms for public health events, triggered alerts for rapid epidemiological investigations and facilitated the monitoring of health events. Results: During the deployment period (4 July–31 August 2019), a total of 121 automated alarms were generated, of which 2 events (heat-related illnesses and injuries/trauma) were confirmed by the response teams. Conclusion: The surveillance system potentially improved the timeliness and situational awareness for health events, including non-infectious threats. In the context of the current COVID-19 pandemic, a health early warning system could enhance case detection and facilitate monitoring of the disease geographical spread and the effectiveness of control measures.

Early Warning System for West Nile Virus outbreaks based on Satellite Earth Observation Data

2020 ◽  
Author(s):  
Elisavet Parselia ◽  
Charalambos Kontoes ◽  
Ioannis Kioutsioukis ◽  
Spiros Mourelatos ◽  
Christos Hadjichristodoulou ◽  
...  
Keyword(s):  
West Nile Virus ◽  
Satellite Data ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Well Being ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Observation Data ◽  
West Nile

<p>The aim of this study is the development of an operational Early Warning System (EWS) that will utilize new and enhanced satellite Earth Observation (EO) sensors with the purpose of forecasting and risk mapping the West Nile Virus (WNV) outbreaks. Satellite EO data were leveraged to estimate environmental variables that influence the transmission cycle of the pathogen that leads to WNV, a mosquito-borne disease (MBD). The system was trained with epidemiological and entomological data from the region of Central Macedonia, the most epidemic-prone region in Greece regarding the WNV. The satellite derived environmental parameters of the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Land Surface Temperature (LST), precipitation data as well as proximity to water bodies were coupled with meteorological data and were used as explanatory variables for the models. The management and analysis of the big satellite data was conducted with the Open Data Cube (ODC), providing an open and freely accessible exploitation architecture. Statistical and machine learning algorithms were used for short-term forecast, while dynamical models were utilized for the seasonal forecast.The system explores the analysis of big satellite data and proves its scalability by replicating the same models in different geographic regions; e.g the northeastern Italian region of Veneto. This EWS will be used as a tool for helping local decision-makers to improve health system responses, take preventive measures in order to curtail the spread of WNV in Europe and address the relevant priorities of the Sustainable Development Goals (SDGs) such as good health and well-being (SDG 3) and climate action (SDG 13).</p>

scholarly journals Building Capacity for a User-Centred Integrated Early Warning System for Drought in Papua New Guinea

2021 ◽  
Vol 13 (16) ◽  
pp. 3307
Author(s):  
Jessica Bhardwaj ◽  
Yuriy Kuleshov ◽  
Zhi-Weng Chua ◽  
Andrew B. Watkins ◽  
Suelynn Choy ◽  
...  
Keyword(s):  
Papua New Guinea ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
New Guinea ◽  
Well Being ◽  
Lead Times ◽  
Pacific Island ◽  
Provincial Level ◽  
Standardised Precipitation Index

Drought has significant impacts on the agricultural productivity and well-being of Pacific Island communities. In this study, a user-centred integrated early warning system (I-EWS) for drought was investigated for Papua New Guinea (PNG). The I-EWS combines satellite products (Standardised Precipitation Index and Vegetation Health Index) with seasonal probabilistic forecasting outputs (chance of exceeding median rainfall). Internationally accepted drought thresholds for each of these inputs are conditionally combined to trigger three drought early warning stages—”DROUGHT WATCH”, “DROUGHT ALERT” and “DROUGHT EMERGENCY”. The developed I-EWS for drought was used to examine the evolution of a strong El Niño-induced drought event in 2015 as well as a weaker La Niña-induced dry period in 2020. Examining the evolution of drought early warnings at a provincial level, it was found that tailored warning lead times of 3–5 months could have been possible for several impacted PNG provinces. These lead times would enable increasingly proactive drought responses with the potential for prioritised allocation of funds at a provincial level. The methodology utilised within this study uses inputs that are openly and freely available globally which indicates promising potential for adaptation of the developed user-centred I-EWS in other Pacific Island Countries that are vulnerable to drought.

scholarly journals Best Paper Selection

2021 ◽  
Vol 30 (01) ◽  
pp. 282-282
Keyword(s):  
Primary Care ◽  
Deep Learning ◽  
Disease Surveillance ◽  
Early Warning System ◽  
Communicable Disease ◽  
Warning System ◽  
Surveillance Systems ◽  
Health Records ◽  
Community Perspectives ◽  
Risk Patients

Zheng L, Wang O, Hao S, Ye C, Liu M, Xia M, Sabo AL, Markovic L, Stearns F, Kanov L, Sylvester KL, Widen R, McElhinney DB, Zhang W, Liao J, Ling XB. Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033212/ Roope LSJ, Tonkin-Crine S, Herd N, Michie S, Pouwels KB, Castro-Sanchez E, Sallis A, Hopkins S, Robotham JV, Crook DW, Peto T. Peters M, Butler CC, Walker AS, Wordsworth S. Reducing expectations for antibiotics in primary care: a randomised experiment to test the response to fear-based messages about antimicrobial resistance. https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01553-6 Degeling C, Carter SM, van Oijen AM McAnulty J, Sintchenko V, Braunack-Mayer A, Yarwood T, Johnson J, Gilbert GL. Community perspectives on the benefits and risks of technologically enhanced communicable disease surveillance systems: a report on four community juries. https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-020-00474-6

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