Mobile phone-based surveillance for animal disease in rural communities: implications for detection of zoonoses spillover

Improving the speed of outbreak detection and reporting at the community level are critical in managing the threat of emerging infectious diseases, many of which are zoonotic. The widespread use of mobile phones, including in rural areas, constitutes a potentially effective tool for real-time surveillance of infectious diseases. Using longitudinal data from a disease surveillance system implemented in 1500 households in rural Kenya, we test the effectiveness of mobile phone animal syndromic surveillance by comparing it with routine household animal health surveys, determine the individual and household correlates of its use and examine the broader implications for surveillance of zoonotic diseases. A total of 20 340 animal and death events were reported from the community through the two surveillance systems, half of which were confirmed as valid disease events. The probability of an event being valid was 2.1 times greater for the phone-based system, compared with the household visits. Illness events were 15 times (95% CI 12.8, 17.1) more likely to be reported through the phone system compared to routine household visits, but not death events (OR 0.1 (95% CI 0.09, 0.11)). Disease syndromes with severe presentations were more likely to be reported through the phone system. While controlling for herd and flock sizes owned, phone ownership was not a determinant of using the phone-based surveillance system, but the lack of a formal education, and having additional sources of income besides farming were associated with decreased likelihood of reporting through the phone system. Our study suggests that a phone-based surveillance system will be effective at detecting outbreaks of diseases such as Rift Valley fever that present with severe clinical signs in animal populations, but in the absence of additional reporting incentives, it may miss early outbreaks of diseases such as avian influenza that present primarily with mortality. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

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Towards an integrated animal health surveillance system in Tanzania: making better use of existing and potential data sources for early warning surveillance

BMC Veterinary Research ◽

10.1186/s12917-021-02789-x ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Janeth George ◽

Barbara Häsler ◽

Erick Komba ◽

Calvin Sindato ◽

Mark Rweyemamu ◽

...

Keyword(s):

Early Warning ◽

Surveillance System ◽

Animal Health ◽

Health Surveillance ◽

Surveillance Data ◽

Data Sources ◽

Surveillance Systems ◽

Cross Sectional Survey ◽

Traceability System ◽

Spatial Coverage

Abstract Background Effective animal health surveillance systems require reliable, high-quality, and timely data for decision making. In Tanzania, the animal health surveillance system has been relying on a few data sources, which suffer from delays in reporting, underreporting, and high cost of data collection and transmission. The integration of data from multiple sources can enhance early detection and response to animal diseases and facilitate the early control of outbreaks. This study aimed to identify and assess existing and potential data sources for the animal health surveillance system in Tanzania and how they can be better used for early warning surveillance. The study used a mixed-method design to identify and assess data sources. Data were collected through document reviews, internet search, cross-sectional survey, key informant interviews, site visits, and non-participant observation. The assessment was done using pre-defined criteria. Results A total of 13 data sources were identified and assessed. Most surveillance data came from livestock farmers, slaughter facilities, and livestock markets; while animal dip sites were the least used sources. Commercial farms and veterinary shops, electronic surveillance tools like AfyaData and Event Mobile Application (EMA-i) and information systems such as the Tanzania National Livestock Identification and Traceability System (TANLITS) and Agricultural Routine Data System (ARDS) show potential to generate relevant data for the national animal health surveillance system. The common variables found across most sources were: the name of the place (12/13), animal type/species (12/13), syndromes (10/13) and number of affected animals (8/13). The majority of the sources had good surveillance data contents and were accessible with medium to maximum spatial coverage. However, there was significant variation in terms of data frequency, accuracy and cost. There were limited integration and coordination of data flow from the identified sources with minimum to non-existing automated data entry and transmission. Conclusion The study demonstrated how the available data sources have great potential for early warning surveillance in Tanzania. Both existing and potential data sources had complementary strengths and weaknesses; a multi-source surveillance system would be best placed to harness these different strengths.

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Achievements and Challenges of Classical Swine Fever Eradication in Brazil

Viruses ◽

10.3390/v12111327 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1327

Author(s):

Luís Guilherme de Oliveira ◽

Igor Renan Honorato Gatto ◽

Marina Lopes Mechler-Dreibi ◽

Henrique M. S. Almeida ◽

Karina Sonálio ◽

...

Keyword(s):

Industrial Production ◽

Surveillance System ◽

Animal Health ◽

Classical Swine Fever ◽

Federal District ◽

Surveillance Systems ◽

Free Zone ◽

The North ◽

Passive Surveillance System ◽

Recent Occurrence

Classical swine fever virus (CSFV) causes one of the most critical diseases in the porcine industry worldwide. In Brazil, the first description of the infection was reported in 1888, and the national recognition of the first free zone (FZ) occurred in 2001. Brazil has been recently recognized (2015–2016) by the World Organisation for Animal Health (OIE) with an FZ involving 15 states and the Federal District, corresponding to 95% of the industrial production of pigs in the country, and a non-free zone (NFZ), comprised by the North and Northeast regions of the country, with approximately 18% of the national pig herd and 5% of industrial production. This review aims to describe the history, the control and eradication actions, the recent occurrence of outbreaks in the NFZ, and the results obtained by the surveillance systems’ action in the FZ for CSF in Brazil since its creation. In the passive surveillance system, the notification of the suspect cases of classical swine fever (CSF) is mandatory while in the active surveillance system adopted in the FZ consists of serological monitoring of certified swine breeding farms (CSBFs), intensive pig farming (IPF), non-technified pig herds (NTPig), surveillance in slaughterhouses and monitoring the populations of wild pigs. In this region, the last outbreaks of the disease occurred in 1998, while in the NFZ, 28 outbreaks were detected from 2005 to 2017, with an apparent lethality rate of 93.96% (840/894). However, in 2018 and 2019, 68 new outbreaks were registered with an apparent lethality rate of 75.05% (1095/1459). Therefore, in 2019, the Brazil CSF-Free Strategic Plan was created to eradicate the infection from the country’s NFZ, since outbreaks in this region present a risk of reintroducing the disease FZ. Finally, differences in characteristics between the regions show factors that still need to be considered for the construction of a robust surveillance system in the NFZ and some improvements in the FZ. Thus, the control of CSF throughout the Brazilian territory requires strict sanitary guidelines, promoting animal health and, consequently, the national production chain’s competitiveness.

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Evaluation the Performance of Exponentially Weighted Moving Average in the Detection of Cholera Outbreaks: Using the Reported Cholera Outbreaks in Literature

Journal of Biostatistics and Epidemiology ◽

10.18502/jbe.v5i3.3617 ◽

2020 ◽

Author(s):

Yousef Alimohamadi ◽

Firooz Esmaeilzadeh ◽

Parisa Jalali ◽

Mohsen Mohammadi ◽

Mojtaba Sepandi

Keyword(s):

Infectious Diseases ◽

Moving Average ◽

The Other ◽

Outbreak Detection ◽

Surveillance Systems ◽

Exponentially Weighted Moving Average ◽

Weighted Moving Average ◽

Exponentially Weighted

Introduction: Timely Detection of outbreaks of infectious diseases can have a very important role in surveillance systems. the presence of appropriate methods can have a very important role for this purpose, the aim of the current study was to Evaluation The Performance of Exponentially Weighted Moving Average in the detection of cholera outbreaks using the reported cholera outbreaks in literature Methods: In the current study the EWMA method was evaluated. To assess the performance of the mentioned methods the six real outbreaks algorithm reported in the literature were used. These reported outbreaks were the daily counts of cholera cases in different countries. After insertion of each outbreak, 7 days inserted as nonoutbreaks days. All analyses performed by MedCalc18.11, Stata version15 and excel 2010. Results: the sensitivity of EWMA was 56.4% (95% CI: 54.3%- 58.5%). The highest sensitivity for outbreak detection was seen in EWMA1 79.18(73.56-84.09) and the lowest was seen in EWMA4 12.2(8.4-17.0). EWMA2 with λ= 0.2 had the best performance with sensitivity 69.8 (63.6-75.5) and specificity 91.4(76.9-98.2) and AUC= 0.80. nd AUC= 0.80. Conclusion: The EWMA method can be very useful in the detection of outbreaks, but the use of this method along the other models may increase the sensitivity of outbreaks detection.

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Enhanced One Health Surveillance during the 58th Presidential Inauguration—District of Columbia, January 2017

Disaster Medicine and Public Health Preparedness ◽

10.1017/dmp.2019.38 ◽

2019 ◽

Vol 14 (2) ◽

pp. 201-207

Author(s):

Tiana A. Garrett-Cherry ◽

Andrew K. Hennenfent ◽

Sasha McGee ◽

John Davies-Cole

Keyword(s):

Syndromic Surveillance ◽

Health Care Systems ◽

Animal Health ◽

Clinical Signs ◽

Health Surveillance ◽

Local Health ◽

Surveillance Systems ◽

Syndromic Surveillance System ◽

Medical Aid ◽

Care Systems

ABSTRACTObjective:In January 2017, Washington, DC, hosted the 58th United States presidential inauguration. The DC Department of Health leveraged multiple health surveillance approaches, including syndromic surveillance (human and animal) and medical aid station–based patient tracking, to detect disease and injury associated with this mass gathering.Methods:Patient data were collected from a regional syndromic surveillance system, medical aid stations, and an internet-based emergency department reporting system. Animal health data were collected from DC veterinary facilities.Results:Of 174 703 chief complaints from human syndromic data, there were 6 inauguration-related alerts. Inauguration attendees who visited aid stations (n = 162) and emergency departments (n = 180) most commonly reported feeling faint/dizzy (n = 29; 17.9%) and pain/cramps (n = 34;18.9%). In animals, of 533 clinical signs reported, most were gastrointestinal (n = 237; 44.5%) and occurred in canines (n = 374; 70.2%). Ten animals that presented dead on arrival were investigated; no significant threats were identified.Conclusion:Use of multiple surveillance systems allowed for near-real-time detection and monitoring of disease and injury syndromes in humans and domestic animals potentially associated with inaugural events and in local health care systems.

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Evaluation of the Kenya Livestock and Wildlife Syndromic Surveillance System for Rift Valley Fever, Narok County, Kenya, 2018-2019

10.21203/rs.3.rs-900525/v1 ◽

2021 ◽

Author(s):

Erenius Nakadio ◽

Samuel Kahariri ◽

Maurice Owiny

Keyword(s):

Data Quality ◽

Rift Valley ◽

Surveillance System ◽

Rift Valley Fever ◽

Participation Rate ◽

Categorical Variables ◽

Surveillance Systems ◽

Continuous Variables ◽

Valley Fever ◽

Syndromic Surveillance System

Abstract Background Rift Valley Fever (RVF) outbreaks in livestock have had a detrimental impact on livestock trade, animal breeding, and productivity. Routine evaluation and data analysis of surveillance systems ensure that health events are efficiently and effectively monitored. This study evaluated Kenya Livestock and Wildlife Surveillance System (KLWSS) and characterized RVF cases reported for Narok County. Methods We evaluated KLWSS from January 2018 to December 2019 using CDC guidelines for evaluating surveillance systems. Attributes of simplicity, flexibility, data quality, acceptability, representativeness, timeliness, stability, sensitivity, and predictive value positive were examined. A retrospective review of RVF surveillance data for Narok County was performed. Demographic and clinical variables were assessed. Data were cleaned in Ms. Excel and descriptive analysis was done using Epi Info 7. Categorical variables were summarized using frequencies and proportions while continuous variables were summarized using measures of central tendency and dispersion. Study authorization was granted by the Directorate of Veterinary Services. Results System was simple in structure and operation, accommodated upgrading of its application, data quality performance was 69.8%, stakeholder’s participation rate was 80% with 842 reports coming from six sub-counties and 30 wards. The median time between event occurrence and event reporting was two days (range one to six days). The system had been operational since 2018 with no reports of any unscheduled outages and downtimes. Suspected cases of RVF reported were 11% (95/842) of the reported cases. The livestock species affected were cattle 56% (53/95) and Sheep 44% (42/95). About 96% (91/95) of the suspected cases were in mixed livestock production systems. The common syndrome was abortions 74% (95/129) with Loita ward recording 97% (92/95) suspected RVF cases. All suspected cases were reported in March 2018. Conclusions The KLWSS system was found to be stable but with below-par performance in data quality. Improvement in data quality is required to ensure that the surveillance system is efficient and effective.

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Public Health Surveillance System

Advances in Medical Technologies and Clinical Practice - Diagnostic Applications of Health Intelligence and Surveillance Systems ◽

10.4018/978-1-7998-6527-8.ch010 ◽

2021 ◽

pp. 201-220

Author(s):

Manish Kumar Dwivedi ◽

Suvashish Kumar Pandey ◽

Prashant Kumar Singh

Keyword(s):

Public Health ◽

Infectious Disease ◽

Infectious Diseases ◽

Surveillance System ◽

Disease Surveillance ◽

Performance Measure ◽

Surveillance Systems ◽

Global Public Health ◽

Global Coverage ◽

Public Health Surveillance System

To guard people against some grave infectious disease, the surveillance system is a key performance measure of global public health threats and vulnerability. The diseases surveillance system helps in public health monitor, control, and prevent infectious diseases. Infectious diseases remain major causes of death. It's important to monitor and surveillance worldwide for developing a framework for risk assessment and health regulation. Surveillance systems help us in understanding the factors driving infectious disease and developing new technological aptitudes with modeling, pathogen determination, characterization, diagnostics, and communications. This chapter discussed surveillance system working, progress toward global public healthy society considering perspectives for the future and improvement of infectious disease surveillance without limited and fragmented capabilities, and making even global coverage.

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Poison Control Centers' Role in Glow Product-Related Outbreak Detection: Implications for Comprehensive Surveillance System

Prehospital and Disaster Medicine ◽

10.1017/s1049023x00006567 ◽

2009 ◽

Vol 24 (1) ◽

pp. 68-72 ◽

Cited By ~ 5

Author(s):

Alvin F. Chu ◽

Steven M. Marcus ◽

Bruce Ruck

Keyword(s):

Surveillance System ◽

Emerging Infectious Diseases ◽

The United States ◽

Outbreak Detection ◽

Poison Control ◽

Surveillance Systems ◽

Clinical Effects ◽

Code Number ◽

Poison Control Centers ◽

Comprehensive Surveillance

AbstractIntroduction:The development of syndromic surveillance systems to detect bioterrorist attacks and emerging infectious diseases has become an important and challenging goal to many governmental agencies and healthcare authorities. This study utilized the sharp increase of glow product-related calls to demonstrate the utility of poison ontrol data for early detection of potential outbreaks during the week of Halloween in 2007.Methods:A review was conducted of the electronic records of exposures reported to the New Jersey Poison Information and Education System NJPIES) Poison Control Hotline from 2002 through 2007 with generic code number 0201027 (glow products) set by the American Association of Poison Control Centers (AAPCC). Key information such as age, gender, time of the call, exposure reason, clinical effects, and medical outcomes along with telephone number, zip code, and county location were used in the analyses to determine the extent of the outbreak.Results:Analyses included a total of 139 glow product-related calls during the week of Halloween in 2007 with a single-day high of 59 calls on Halloween Day. More than 90% of the glow product exposures were in children 1–10 years of age. The glow product-related calls on Halloween Day increased from 14 calls in 2002 to 59 calls in 2007, a 321% increase during a six-year period.Conclusions:Poison control centers in the United States are equipped with a unique and uniform input data collection system—the National Poison Data System—that provides an important data source in the development of a comprehensive surveillance system for early outbreak detection.

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Evaluating measles surveillance: comparison of sentinel surveillance, mandatory notification, and data from health insurance claims

Epidemiology and Infection ◽

10.1017/s095026881000244x ◽

2010 ◽

Vol 139 (4) ◽

pp. 516-523 ◽

Cited By ~ 7

Author(s):

S. TANIHARA ◽

E. OKAMOTO ◽

T. IMATOH ◽

Y. MOMOSE ◽

A. KAETSU ◽

...

Keyword(s):

Health Insurance ◽

Infectious Diseases ◽

Surveillance System ◽

Sentinel Surveillance ◽

Surveillance Systems ◽

Insurance Claims ◽

Notification System ◽

Mandatory Notification ◽

Health Insurance Claims ◽

Sentinel Surveillance System

SUMMARYInadequate notification is a recognized problem of measles surveillance systems in many countries, and it should be monitored using multiple data sources. We compared data from three different surveillance sources in 2007: (1) the sentinel surveillance system mandated by the Act on Prevention of Infectious Diseases and Medical Care for Patients Suffering Infectious Diseases, (2) the mandatory notification system run by the Aichi prefectural government, and (3) health insurance claims (HICs) submitted to corporate health insurance societies. For each dataset, we examined the number of measles cases by month, within multiple age groups, and in two categories of diagnostic test groups. We found that the sentinel surveillance system underestimated the number of adult measles cases. We also found that HIC data, rather than mandatory notification data, were more likely to come from individuals who had undergone laboratory tests to confirm their measles diagnosis. Thus, HIC data may provide a supplementary and readily available measles surveillance data source.

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Canadian Dairy Network for Antimicrobial Stewardship and Resistance (CaDNetASR): An On-Farm Surveillance System

Frontiers in Veterinary Science ◽

10.3389/fvets.2021.799622 ◽

2022 ◽

Vol 8 ◽

Author(s):

Mariana Fonseca ◽

Luke C. Heider ◽

David Léger ◽

J. Trenton Mcclure ◽

Daniella Rizzo ◽

...

Keyword(s):

Antimicrobial Resistance ◽

Antimicrobial Stewardship ◽

Surveillance System ◽

Animal Health ◽

Dairy Farms ◽

Essential Elements ◽

Surveillance Systems ◽

Herd Level ◽

E Coli ◽

On Farm

Canada has implemented on-farm antimicrobial resistance (AMR) surveillance systems for food-producing animals under the Canadian Integrated Program for Antimicrobial Resistance (CIPARS); however, dairy cattle have not been included in that program yet. The objective of this manuscript was to describe the development and implementation of the Canadian Dairy Network for Antimicrobial Stewardship and Resistance (CaDNetASR). An Expert Panel (EP) of researchers was created to lead the development of the dairy surveillance system. The EP initiated a draft document outlining the essential elements of the surveillance framework. This document was then circulated to a Steering Committee (SC), which provided recommendations used by the EP to finalize the framework. CaDNetASR has the following components: (1) a herd-level antimicrobial use quantification system; (2) annually administered risk factor questionnaires; and (3) methods for herd-level detection of AMR in three sentinel enteric pathogens (generic Escherichia coli, Campylobacter spp., and Salmonella spp.) recovered from pooled fecal samples collected from calves, heifers, cows, and the manure pit. A total of 144 dairy farms were recruited in five Canadian provinces (British-Columbia, Alberta, Ontario, Québec, and Nova-Scotia), with the help of local herd veterinarians and regional field workers, and in September 2019, the surveillance system was launched. 97.1 and 94.4% of samples were positive for E. coli, 63.8, and 49.1% of samples were positive for Campylobacter spp., and 5.0 and 7.7% of samples were positive for Salmonella spp., in 2019 and 2020, respectively. E. coli was equally distributed among all sample types. However, it was more likely that Campylobacter spp. were recovered from heifer and cow samples. On the other hand, it was more common to isolate Salmonella spp. from the manure pit compared to samples from calves, heifers, or cows. CaDNetASR will continue sampling until 2022 after which time this system will be integrated into CIPARS. CaDNetASR will provide online access to farmers and veterinarians interested in visualizing benchmarking metrics regarding AMU practices and their relationship to AMR and animal health in dairy herds. This will provide an opportunity to enhance antimicrobial stewardship practices on dairy farms in Canada.

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High real-time reporting of domestic and wild animal diseases following rollout of mobile phone reporting system in Kenya

PLoS ONE ◽

10.1371/journal.pone.0244119 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0244119

Author(s):

M. Kariuki Njenga ◽

Naomi Kemunto ◽

Samuel Kahariri ◽

Lindsey Holmstrom ◽

Harry Oyas ◽

...

Keyword(s):

Mobile Phone ◽

Open Source ◽

Reporting System ◽

Skin Diseases ◽

Animal Health ◽

Wild Animal ◽

Sub Saharan Africa ◽

Surveillance Systems ◽

Animal Disease ◽

Disease Reporting

Background To improve early detection of emerging infectious diseases in sub-Saharan Africa (SSA), many of them zoonotic, numerous electronic animal disease-reporting systems have been piloted but not implemented because of cost, lack of user friendliness, and data insecurity. In Kenya, we developed and rolled out an open-source mobile phone-based domestic and wild animal disease reporting system and collected data over two years to investigate its robustness and ability to track disease trends. Methods The Kenya Animal Biosurveillance System (KABS) application was built on the Java® platform, freely downloadable for android compatible mobile phones, and supported by web-based account management, form editing and data monitoring. The application was integrated into the surveillance systems of Kenya’s domestic and wild animal sectors by adopting their existing data collection tools, and targeting disease syndromes prioritized by national, regional and international animal and human health agencies. Smartphone-owning government and private domestic and wild animal health officers were recruited and trained on the application, and reports received and analyzed by Kenya Directorate of Veterinary Services. The KABS application performed automatic basic analyses (frequencies, spatial distribution), which were immediately relayed to reporting officers as feedback. Results Of 697 trained domestic animal officers, 662 (95%) downloaded the application, and >72% of them started reporting using the application within three months. Introduction of the application resulted in 2- to 14-fold increase in number of disease reports when compared to the previous year (relative risk = 14, CI 13.8–14.2, p<0.001), and reports were more widely distributed. Among domestic animals, food animals (cattle, sheep, goats, camels, and chicken) accounted for >90% of the reports, with respiratory, gastrointestinal and skin diseases constituting >85% of the reports. Herbivore wildlife (zebra, buffalo, elephant, giraffe, antelopes) accounted for >60% of the wildlife disease reports, followed by carnivores (lions, cheetah, hyenas, jackals, and wild dogs). Deaths, traumatic injuries, and skin diseases were most reported in wildlife. Conclusions This open-source system was user friendly and secure, ideal for rolling out in other countries in SSA to improve disease reporting and enhance preparedness for epidemics of zoonotic diseases.

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