scholarly journals A Timeliness Study of Disease Surveillance Data Post ELR Implementation in Houston

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Kasimu Muhetaer ◽  
Eunice R. Santos ◽  
Avi Raju ◽  
Kiley Allred ◽  
Biru Yang ◽  
...  

After ELR implementation in Houston, the annual number of cases and number of reportable cases increased substantially (chart1); prior to the ELR implementation it took longer to report a case. The use of electronic disease surveillance system and the implementation of ELR improved the Houston disease surveillance system capacity of early case detection (table1); however, after ELR implementation, probably due to increase in case volume, it took longer to complete an investigation (table2); not substantial differences were found between cases pre and post ELR implementation, but cases populated by ELRs were less complete with case reporting information (table3).

2020 ◽  
Author(s):  
Mehnaz Adnan ◽  
Xiaoying Gao ◽  
Xiaohan Bai ◽  
Elizabeth Newbern ◽  
Jill Sherwood ◽  
...  

BACKGROUND Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. OBJECTIVE The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. METHODS We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the affected area. RESULTS Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. CONCLUSIONS Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.


10.2196/18281 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e18281
Author(s):  
Mehnaz Adnan ◽  
Xiaoying Gao ◽  
Xiaohan Bai ◽  
Elizabeth Newbern ◽  
Jill Sherwood ◽  
...  

Background Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. Objective The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. Methods We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the affected area. Results Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. Conclusions Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.


Author(s):  
Henry Chidawanyika ◽  
Ponesai Nyika ◽  
Joshua Katiyo ◽  
Anthony Sox ◽  
Tongai Chokuda ◽  
...  

Innovative approach to revitalizing Disease Surveillance System in Zimbabwe using cell-phone mediated data transmission has been a huge success. Cell phones have been successfully integrated into disease surveillance system resulting in expansion of surveillance coverage, improved completeness and timeliness. Decision makers are now able to access disease surveillance data in near real-time.


Author(s):  
Mohammed Husain ◽  
Mahmudur Rahman ◽  
Asm Alamgir ◽  
M. Salim Uzzaman ◽  
Meerjady Sabrina Flora

Objectivea) To observe trends and patterns of diseases of public health importance and responseb) To predict, prevent, detect, control and minimize the harm caused by public health emergenciesc) To develop evidence for managing any future outbreaks, epidemic and pandemicIntroductionDisease surveillance is an integral part of public health system. It is an epidemiological method for monitoring disease patterns and trends. International Health Regulation (IHR) 2005 obligates WHO member countries to develop an effective disease surveillance system. Bangladesh is a signatory to IHR 2005. Institute of Epidemiology, Disease Control and Research (IEDCR <www.iedcr.gov.bd>) is the mandated institute for surveillance and outbreak response on behalf of Government of the People’s Republic of Bangladesh. The IEDCR has a good surveillance system including event-based surveillance system, which proved effective to manage public health emergencies. Routine disease profile is collected by Management Information System (MIS) of Directorate General of Health Services (DGHS). Expanded Program of Immunization (EPI) of DGHS collect surveillance data on EPI-related diseases. Disease Control unit, DGHS is responsible for implementing operational plan of disease surveillance system of IEDCR. The surveillance system maintain strategic collaboration with icddrr,b.MethodsThe IEDCR is conducting disease surveillance in several methods and following several systems. Surveillance data of priority communicable disease are collected by web based integrated disease surveillance. It is based on weekly data received from upazilla (sub-district) health complex on communicable disease marked as priority. They are: acute watery diarrhea, bloody dysentery, malaria, kala-azar, tuberculosis, leprosy, encephalitis, any unknown disease. Government health facilities at upazilla (sub-district) send the data using DHIS2. During outbreak, daily, even hourly reporting is sought from the concerned unit.Moreover, IEDCR conducts disease specific specialized surveillance systems. Data from community as well as from health facilities are collected for Influenza, nipah, dengue, HIV, cholera, cutaneous anthrax, non-communicable diseases, food borne illness. Data from health facilities are collected for antimicrobial resistance, rotavirus and intussusception, reproductive health, child health and mortality, post MDA-surveillance for lymphatic filariasis transmission, molecular xenomonitoring for detection of residual Wucheria bancrofti, dengue (virological), emerging zoonotic disease threats in high-risk interfaces, leptospirosis, acute meningo-encephalitis syndrome (AMES) focused on Japanese encephalitis and nipah, unintentional acute pesticide poisoning among young children. Data for event based surveillance are collected from usual surveillance system as well as from dedicated hotlines (24/7) of IEDCR, media monitoring, and any informal reporting.Case detection is done by syndromic surveillance, laboratory diagnosed surveillance, media surveillance, hotline, cell phone-based surveillance. Dissemination of surveillance is done by website of IEDCR, periodic bulletins, seminar, conference etc. Line listing are done by rapid response teams working in the surveillance sites. Demographic information and short address are listed in the list along with clinical and epidemiological information. Initial cases are confirmed by laboratory test, if required from collaborative laboratory at US CDC (Atlanta). When the epidemiological trend is clear, then subsequent cases are detected by symptoms and rapid tests locally available.ResultsIn 2017, 26 incidents of disease outbreak were investigated by National Rapid Response Team (NRRT) of IEDCR. In the same year, 12 cases of outbreak of unknown disease was investigated by NRRT of IEDCR at different health facilities. Joint surveillance with animal health is being planned for detection and managing zoonotic disease outbreaks, following One Health principles. Department of Livestock, Ministry of Environment and icddrb are partners of the joint surveillance based on One Health principles.Disease Control unit of DGHS, district and upazilla health managers utilizes the disease surveillance data for public health management. They analyze also the surveillance data at their respective level to serve their purpose.ConclusionsA robust surveillance is necessary for assessing the public health situation and prompt notification of public health emergency. The system was introduced at IEDCR mainly for malaria and diarrhea control during establishment of this institute. Eventually the system was developed for communicable disease, and recently for non-communicable diseases. It is effectively used for managing public health emergencies. Notification and detection of public health emergency is mostly possible due to media surveillance.Data for syndromic surveillance for priority communicable diseases is often not sent timely and data quality is often compromised. Tertiary hospitals are yet to participate in the web based integrated disease surveillance system for priority communicable diseases. But they are part of specialized disease surveillances. Data from specialized surveillance with laboratory support is of high quality.Evaluation of the system by conducting research is recommended to improve the system. Specificity and sensitivity of case detection system should also be tested periodically.ReferencesCash, Richard A, Halder, Shantana R, Husain, Mushtuq, Islam, Md Sirajul, Mallick, Fuad H, May, Maria A, Rahman, Mahmudur, Rahman, M Aminur. Reducing the health effect of natural hazards in Bangladesh. Lancet, The, 2013, Volume 382, Issue 9910IEDCR. At the frontline of public health. updated 2013. www.iedcr.gov.bdAo TT, Rahman M et al. Low-Cost National Media-Based Surveillance System for Public Health Events, Bangladesh. Emerging Infectious Diseases. Vol 22, No 4. 2016.<www.iedcr.gov.bd> accessed on 1 Oct 2018. 


2021 ◽  
Vol 64 (5) ◽  
pp. 338-357
Author(s):  
Natalie Troke ◽  
Chloë Logar‐Henderson ◽  
Nathan DeBono ◽  
Mamadou Dakouo ◽  
Selena Hussain ◽  
...  

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