Detection Of Disease Outbreaks Research Articles

An integer-valued time series model for multivariate surveillance.

In recent days, different types of surveillance data are becoming available for public health purposes. In most cases, several variables are monitored and events of different types are reported. As the amount of surveillance data increases, statistical methods that can effectively address multivariate surveillance scenarios are demanded. Even though research activity in this field is increasing rapidly in recent years, only a few approaches have simultaneously addressed the integer-valued property of the data and its correlation (both time correlation and cross-correlation) structure. In this article, we suggest a multivariate integer-valued autoregressive model that allows for both serial and cross-correlations between the series and can easily accommodate overdispersion and covariate information. Moreover, its structure implies a natural decomposition into an endemic and an epidemic component, a common distinction in dynamic models for infectious disease counts. Detection of disease outbreaks is achieved through the comparison of surveillance data with one-step-ahead predictions obtained after fitting the suggested model to a set of clean historical data. The performance of the suggested model is illustrated on a trivariate series of syndromic surveillance data collected during Athens 2004 Olympic Games.

One Health workers: innovations in early detection of human, animal, and plant disease outbreaks

The majority (60%) of human pathogens are zoonotic and 75% of emerging zoonotic pathogens are from wildlife species (eg, Ebola, Zika). In addition, vector, water, and food-borne diseases greatly impact people, livestock, and agriculture and result in adverse effects on local, national, and global economies. Continuous monitoring and surveillance of newly emerging diseases and pandemic outbreaks is a critical component in One Health (OH) which is the term representing the collaboration of multiple disciplines working to achieve optimal health for people, animals, plants/crops, and the environment. For decades a group of minimally trained local workers --community health workers -- (CHWs) have functioned at the interface between community members and whatever health care system existed within a country. Today, around the world CHWs have become the bedrock and frontline health care providers as they deliver or monitor human treatments, engage in the detection of common illness, and offer preventive care. In these same communities there is often severely limited access to veterinary health services which has a negative impact on livestock production with implications for food security, and has led to the inappropriate and indiscriminate use of antibiotics. In rural areas poor field maintenance and common agricultural health problems results in the death or decreased crop production of important food sources. Inappropriate use of pesticides and other products that are toxic to animals and humans often enter the soil or water supply through misuse. This lack of health integration across human, animal, plant, and environmental health sectors in an ever-shrinking world begs for a shift in health services and the creation of a community based One Health Worker.

A real-time spatio-temporal syndromic surveillance system with application to small companion animals

Lack of disease surveillance in small companion animals worldwide has contributed to a deficit in our ability to detect and respond to outbreaks. In this paper we describe the first real-time syndromic surveillance system that conducts integrated spatio-temporal analysis of data from a national network of veterinary premises for the early detection of disease outbreaks in small animals. We illustrate the system’s performance using data relating to gastrointestinal disease in dogs and cats. The data consist of approximately one million electronic health records for dogs and cats, collected from 458 UK veterinary premises between March 2014 and 2016. For this illustration, the system predicts the relative reporting rate of gastrointestinal disease amongst all presentations, and updates its predictions as new data accrue. The system was able to detect simulated outbreaks of varying spatial geometry, extent and severity. The system is flexible: it generates outcomes that are easily interpretable; the user can set their own outbreak detection thresholds. The system provides the foundation for prompt detection and control of health threats in companion animals.

Language Specific Gaps in Identifying Early Epidemic Signals – A Case Study of the Malay Language

Background: Internet-based surveillance systems have become invaluable tools to complement traditional surveillance in the early detection of infectious disease outbreaks. Search results limited to only English-language sources may lead to missed opportunities in global outbreak monitoring efforts. The Malay language has 290 million native speakers across the Southeast Asian region (Indonesia, Malaysia, Brunei, Singapore and the southern parts of Thailand and the Philippines), which is an important region for outbreaks. The aim of this research is to determine the completeness of reports of Malay language outbreak detection by commonly used open source surveillance platforms. Methods: We searched ProMED-mail, HealthMap, and Google News to investigate outbreak events in Malaysia and Indonesia between 1st August 2016 and 31st August 2017. We also cross-checked published epidemic reports in Medline to determine the proportion of outbreaks that are published. Results: A total of 371 entries from ProMED-mail and HealthMap were included. A Google News search query using Malay language keywords resulted in 453 news outbreak reports, 98 of which were missed by either or both ProMED-mail and HealthMap. During the study time frame, only 40 published epidemic reports were found in MEDLINE, showing the importance of informal data sources to identify outbreaks. Some diseases such as mumps, dengue and avian influenza had different local terminology in Indonesia and Malaysia. Conclusions: Search results limited to only English-language sources may lead to reduced outbreak detection. The inclusion of keywords in the Malay language improves epidemic intelligence in the region, but needs to be nuanced for local terminology, which may differ between countries. Informal Internet-based surveillance can improve compliance with International Health Regulations (IHR) for low income countries, because it is less resource-intensive than formal surveillance. However, there is a need to include local language search terms to improve outbreak detection, especially in regions at high risk of emerging infectious diseases.

Utility of emergency call centre, dispatch and ambulance data for syndromic surveillance of infectious diseases: a scoping review.

BackgroundSyndromic surveillance can supplement conventional health surveillance by analyzing less-specific, near-real-time data for an indication of disease occurrence. Emergency medical call centre dispatch and ambulance data are examples of routinely and efficiently collected syndromic data that might assist in infectious disease surveillance. Scientific literature on the subject is scarce and an overview of results is lacking.MethodsA scoping review including (i) review of the peer-reviewed literature, (ii) review of grey literature and (iii) interviews with key informants.ResultsForty-four records were selected: 20 peer reviewed and 24 grey publications describing 44 studies and systems. Most publications focused on detecting respiratory illnesses or on outbreak detection at mass gatherings. Most used retrospective data; some described outcomes of temporary systems; only two described continuously active dispatch- and ambulance-based syndromic surveillance. Key informants interviewed valued dispatch- and ambulance-based syndromic surveillance as a potentially useful addition to infectious disease surveillance. Perceived benefits were its potential timeliness, standardization of data and clinical value of the data.ConclusionsVarious dispatch- and ambulance-based syndromic surveillance systems for infectious diseases have been reported, although only roughly half are documented in peer-reviewed literature and most concerned retrospective research instead of continuously active surveillance systems. Dispatch- and ambulance-based syndromic data were mostly assessed in relation to respiratory illnesses; reported use for other infectious disease syndromes is limited. They are perceived by experts in the field of emergency surveillance to achieve time gains in detection of infectious disease outbreaks and to provide a useful addition to traditional surveillance efforts.

Unweaving tangled mortality and antibiotic consumption data to detect disease outbreaks - Peaks, growths, and foresight in swine production.

As our capacity to collect and store health data is increasing, a new challenge of transforming data into meaningful information for disease monitoring and surveillance has arisen. The aim of this study was to explore the potential of using livestock mortality and antibiotic consumption data as a proxy for detecting disease outbreaks at herd level. Changes in the monthly records of mortality and antibiotic consumption were monitored in Danish swine herds that became positive for porcine reproductive and respiratory syndrome (PRRS) and porcine pleuropneumonia. Laboratory serological results were used to identify herds that changed from a negative to a positive status for the diseases. A dynamic linear model with a linear growth component was used to model the data. Alarms about state changes were raised based on forecast errors, changes in the growth component, and the values of the retrospectively smoothed values of the growth component. In all cases, the alarms were defined based on credible intervals and assessed prior and after herds got a positive disease status. The number of herds with alarms based on mortality increased by 3% in the 3 months prior to laboratory confirmation of PRRS-positive herds (Se = 0.47). A 22% rise in the number of weaner herds with alarms based on the consumption of antibiotics for respiratory diseases was found 1 month prior to these herds becoming PRRS-positive (Se = 0.22). For porcine pleuropneumonia-positive herds, a 10% increase in antibiotic consumption for respiratory diseases in sow herds was seen 1 month prior to a positive result (Se = 0.5). Monitoring changes in mortality data and antibiotic consumption showed changes at herd level prior to and in the same month as confirmation from diagnostic tests. These results also show a potential value for using these data streams as part of surveillance strategies.

A Nonparametric Approach to Uncovering Connected Anomalies by Tree Shaped Priors

The area of anomaly detection has recently been expanded in the graph-based data. Anomalous vertices are often exhibited as a connected subgraph. Few works, however, have focused on connected anomalous subgraph detection because of the challenge of optimizing graph functionals under connectivity constraints. We employ Non-Parametric Graph Scan (NPGS) statistics for detecting anomalies within graph-based data. Based on the NPGS statistics, we proposed an efficient approximate approach to the connected anomalous subgraph detection problem that provides provable guarantees on performance and quality. In particular, we first decompose the problem into a sequence of subproblems, each of which can be reduced to a Budget Price-Collecting Steiner Tree (B-PCST) problem, and then develop efficient exact and approximate algorithms for a special category of graphs in which the anomalous subgraphs can be reformulated in a fixed tree topology. Our method has a wide variety of applications, such as disease outbreak detection, road traffic congestion detection, and event detection in social media, because the NPGS statistics is free of distribution assumptions and can be applied to heterogeneous graph data.

A Fully Integrated Real-Time Detection, Diagnosis, and Control of Community Diarrheal Disease Clusters and Outbreaks (the INTEGRATE Project): Protocol for an Enhanced Surveillance System.

BackgroundDiarrheal disease, which affects 1 in 4 people in the United Kingdom annually, is the most common cause of outbreaks in community and health care settings. Traditional surveillance methods tend to detect point-source outbreaks of diarrhea and vomiting; they are less effective at identifying low-level and intermittent food supply contamination. Furthermore, it can take up to 9 weeks for infections to be confirmed, reducing slow-burn outbreak recognition, potentially impacting hundreds or thousands of people over wide geographical areas. There is a need to address fundamental problems in traditional diarrheal disease surveillance because of underreporting and subsequent unconfirmed infection by patients and general practitioners (GPs); varying submission practices and selective testing of samples in laboratories; limitations in traditional microbiological diagnostics, meaning that the timeliness of sample testing and etiology of most cases remains unknown; and poorly integrated human and animal surveillance systems, meaning that identification of zoonoses is delayed or missed.ObjectiveThis study aims to detect anomalous patterns in the incidence of gastrointestinal disease in the (human) community; to target sampling; to test traditional diagnostic methods against rapid, modern, and sensitive molecular and genomic microbiology methods that identify and characterize responsible pathogens rapidly and more completely; and to determine the cost-effectiveness of rapid, modern, sensitive molecular and genomic microbiology methods.MethodsSyndromic surveillance will be used to aid identification of anomalous patterns in microbiological events based on temporal associations, demographic similarities among patients and animals, and changes in trends in acute gastroenteritis cases using a point process statistical model. Stool samples will be obtained from patients’ consulting GPs, to improve the timeliness of cluster detection and characterize the pathogens responsible, allowing health protection professionals to investigate and control outbreaks quickly, limiting their size and impact. The cost-effectiveness of the proposed system will be examined using formal cost-utility analysis to inform decisions on national implementation.ResultsThe project commenced on April 1, 2013. Favorable approval was obtained from the Research Ethics Committee on June 15, 2015, and the first patient was recruited on October 13, 2015, with 1407 patients recruited and samples processed using traditional laboratory techniques as of March 2017.ConclusionsThe overall aim of this study is to create a new One Health paradigm for detecting and investigating diarrhea and vomiting in the community in near-real time, shifting from passive human surveillance and management of laboratory-confirmed infection toward an integrated, interdisciplinary enhanced surveillance system including management of people with symptoms.International Registered Report Identifier (IRRID)DERR1-10.2196/13941

Use of Mobile Apps for epidemic surveillance and response – availability and gaps.

Background: Early detection of disease outbreaks, using appropriate surveillance methods, is a basic principle for effective control of epidemics. Indicator-based surveillance methods, such as comprehensive surveillance, sentinel surveillance and syndromic surveillance, have been routinely utilized for early epidemic detection to minimize mortality and morbidity related to emerging infectious disease threats. In addition, event-based surveillance uses unstructured data sources to detect and monitor outbreaks such as media reports, social media and websites. The use of mobile phone technology is growing in many low and middle-income countries, which has made mHealth an efficient means of health communication in such countries for epidemic surveillance, mitigation and response. Mobile Apps may draw data from validated health sources or unvalidated public sources and convey information to responders. The aim of this study was to review mobile Apps used for epidemic surveillance and response. Methods: A review of Google Play and the App Store was conducted from June 2018 to August 2018 for Apps containing the words “epidemic”, “outbreak”, “pandemic”, “public health”, “infectious diseases”, “infection”, “bioterrorism” or “CBRNE”, “surveillance”. Available Apps were analysed by the intended user, purpose, platform, functions and number of downloads. Results: We found a total of 106 Apps in an initial search, and of those 80 Apps did not meet the selection criteria and were excluded. Finally, 26 relevant surveillance Apps, including 21 free of charge and 5 paid Apps, were included in this review. Of these, 17 Apps are for single disease surveillance, 7 Apps for multiple disease surveillance and 2 Apps provides information on possible bioterrorism agents. The intended user varied from the general public (18 Apps) to health practitioners (4 Apps) and remaining 4 Apps intended for both general public and health practitioners. The Apps included real-time tracking in an interactive map (10 Apps), daily notification alerts (3 Apps), user function to report diseases/ outbreaks (4 Apps) and multiple disease tracking options (7 Apps). The most downloaded Apps were Sickweather with 100,000+ downloads and HealthMap with 10,000+ downloads. Only 4 Apps were available on both Android and iOS platforms. Conclusion: There are at least 26 mobile Apps for surveillance of epidemics, mostly free of charge and mostly for laypeople. Among them, HealthMap is the most comprehensive, but by far the greatest number of downloads was for a consumer App, Sickweather. Some Apps can provide real-time tracking and interactive maps. However, limitations included unavailability of Apps suitable for general public or surveillance of potential bioterrorism incidents, limited geographic or disease relevance, and high cost for some Apps. There is great potential to utilize existing Apps and develop new ones, especially which meet the needs of health professionals and public health authorities for real-time disease surveillance.

Dynamic and integrative approaches to understanding pathogen spillover.

Dynamic and integrative approaches to understanding pathogen spillover.

Assessing the value of PCR assays in oral fluid samples for detecting African swine fever, classical swine fever, and foot-and-mouth disease in U.S. swine.

IntroductionOral fluid sampling and testing offers a convenient, unobtrusive mechanism for evaluating the health status of swine, especially grower and finisher swine. This assessment evaluates the potential testing of oral fluid samples with real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) to detect African swine fever, classical swine fever, or foot-and-mouth disease for surveillance during a disease outbreak and early detection in a disease-free setting.MethodsWe used a series of logical arguments, informed assumptions, and a range of parameter values from literature and industry practices to examine the cost and value of information provided by oral fluid sampling and rRT-PCR testing for the swine foreign animal disease surveillance objectives outlined above.ResultsBased on the evaluation, oral fluid testing demonstrated value for both settings evaluated. The greatest value was in an outbreak scenario, where using oral fluids would minimize disruption of animal and farm activities, reduce sample sizes by 23%-40%, and decrease resource requirements relative to current individual animal sampling plans. For an early detection system, sampling every 3 days met the designed prevalence detection threshold with 0.95 probability, but was quite costly.LimitationsImplementation of oral fluid testing for African swine fever, classical swine fever, or foot-and-mouth disease surveillance is not yet possible due to several limitations and information gaps. The gaps include validation of PCR diagnostic protocols and kits for African swine fever, classical swine fever, or foot-and-mouth disease on swine oral fluid samples; minimal information on test performance in a field setting; detection windows with low virulence strains of some foreign animal disease viruses; and the need for confirmatory testing protocol development.

EpidVis: A visual web querying tool for animal epidemiology surveillance

The use of electronic media for the detection and monitoring of animal disease outbreaks is crucial for disease surveillance and early warning systems. Animal health specialists regularly query web pages using various formulations to obtain up-to-date news on disease outbreaks. This task, however, is often manual and time-consuming. Visualization techniques can nevertheless facilitate their web searches, compared to traditional searches. This article presents EpidVis, a visual web query tool designed for experts in animal health, conducting epidemic intelligence activities from news sources on the Internet. It consists of several views that help the domain experts efficiently build and launch queries, as well as visualize the results. Moreover, it supports external information integration to help domain experts enrich their knowledge and adapt their queries. EpidVis was assessed considering usability (user study) and usefulness for experts (case study). The results show that our tool helps domain experts in their daily surveillance tasks, allowing them to extract in timely manner accurate information on disease outbreaks from the web.

Contact tracing in Lassa fever Outbreak Response, an effective strategy for control?

ObjectiveTo identify risk factors among contacts of Lassa fever cases which can predispose to disease spread and institute control measures.IntroductionContact tracing is an important strategy employed in surveillance to aid prompt detection of infectious disease and control outbreaks. It involves the identification of those who have come in contact with an infectious person and following them up for the duration of the incubation period of the disease to promptly detect symptoms and signs and institute treatment thus reducing chances of disease spread to other susceptible individuals. It is a primary means of disease prevention. The importance of cooperation from contacts being traced cannot be overemphasized as they are required to promptly report symptoms, avoid gatherings and travelling until they are cleared by the surveillance team. The follow-up should also link contacts who become symptomatic to designated care centers. In 2014, during the Ebola outbreak in Nigeria, the disease spread from Lagos to another State in the country by a contact who travelled out of Lagos to Rivers state.To identify risk factors among contacts of Lassa fever cases which can predispose to disease spread and institute control measures, we interviewed primary contacts of Lassa fever cases during an outbreak response in Kogi State Nigeria, 2016MethodsWe Identified contacts of Lassa fever cases (confirmed/suspected/probable) among family, friends, community members, co-workers and health care workers, followed up for 21 days during a Lassa fever outbreak which started in February 2016 at Kogi State Nigeria. Contacts were interviewed using a structured interviewer administered questionnaire with sections on demography, risk factors for infection and spread of Lassa fever, symptoms developed during the follow-up period and adherence to prorocol. Control measures were instituted to address identified gaps. We defined a contact as anyone irrespective of age, occupation or sex who came in contact with any of the cases of Lassa fever classified as either confirmed/suspected/probable and used standard IDSR case definitions for Suspected, Confirmed and Probable cases of Lassa fever. Data was analysed with Epi Info version 7ResultsOverall 149 contacts were interviewed, 79 (53.0%) were female, the mean age of respondents was 33.2 +-10.1 and many were health care workers 61(40.9%). Of the respondents, 18 (12.0%) live or work in areas infested with rodents, 21 (14.1%) ate bush meat, 2 (2.5%) of the females were pregnant and 20 (13.4%) of respondents travelled out of station with 1 (5%) contact crossing international borders. During the follow-up period, 14 (9.4%) developed symptoms suggestive of Lassa fever. Of these 12 (85.7%) sought treatment and the options were self-medication 3 (25.0%) and presenting at a health facility 9 (77.8%). The health facilities visited were mainly privately owned 7(77.9%) and only 1 (11.1) was a tertiary health care facility. We instituted the following interventions; Health education of contacts and linkage of symptomatic contacts to the designated treatment center where treatment commenced and samples were collected and sent to reference lab for diagnosis. All samples 14(100%) came back negative for Lassa fever.ConclusionsContact tracing is an important strategy in controlling outbreaks of infectious diseases. However, risk factors identified among contacts can hinder the effectiveness of this strategy and facilitate spread of the disease. We recommend training on health education and Lassa fever transmission for contacts of cases and the need to adhere to protocol so that the ultimate aim of interrupting transmission can be achieved.ReferencesNigeria Center for Disease Control.Viral Haemorrhagic Fevers,Preparedness and Response Plan.2017

Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change.

A new generation of surveillance strategies is being developed to help detect emerging infections and to identify the increased risks of infectious disease outbreaks that are expected to occur with climate change. These surveillance strategies include event-based surveillance (EBS) systems and risk modelling. The EBS systems use open-source internet data, such as media reports, official reports, and social media (such as Twitter) to detect evidence of an emerging threat, and can be used in conjunction with conventional surveillance systems to enhance early warning of public health threats. More recently, EBS systems include artificial intelligence applications such machine learning and natural language processing to increase the speed, capacity and accuracy of filtering, classifying and analysing health-related internet data. Risk modelling uses statistical and mathematical methods to assess the severity of disease emergence and spread given factors about the host (e.g. number of reported cases), pathogen (e.g. pathogenicity) and environment (e.g. climate suitability for reservoir populations). The types of data in these models are expanding to include health-related information from open-source internet data and information on mobility patterns of humans and goods. This information is helping to identify susceptible populations and predict the pathways from which infections might spread into new areas and new countries. As a powerful addition to traditional surveillance strategies that identify what has already happened, it is anticipated that EBS systems and risk modelling will increasingly be used to inform public health actions to prevent, detect and mitigate the climate change increases in infectious diseases.

Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change

Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change

Using the MCRISP Network to Study Acute Gastroenteritis and Influenza-Like Illness Outbreaks in Child Care Centers Compared to Statewide Epidemics

Introduction:Biosurveillance is critical for early detection of disease outbreaks and resource mobilization. Child care center (CCC) attendance has long been recognized as a significant independent predictor for respiratory and gastrointestinal diseases, but CCC surveillance is currently not part of the statewide disease surveillance system. The Michigan Child Care Related Infections Surveillance Program (MCRISP) is an independent, online reporting network with >30 local CCCs that was created to fill this surveillance gap.Aim:To describe the capability of a novel CCC biosurveillance system (MCRISP) to report pediatric Influenza-Like Illness (ILI) and Acute Gastroenteritis (AGE) illness over three years to (i) assess both the timing and magnitude of epidemics in CCCs and (ii) compare CCC outbreak patterns with those of the state database.Methods:MCRISP collates real-time syndromic reports of illness from local county CCCs. The statewide Michigan Disease Surveillance System (MDSS) collects reports of diagnosed illness from designated laboratories, clinics, and hospitals statewide. We assessed epidemic curves based on MCRISP incidence rates and MDSS case counts for ILI and AGE over three seasons (2014-7).Results:A total of 4,627 MCRISP cases (2,425 ILI and 2,202 AGE reports) were reported during the three years of study surveillance. Epidemic patterns (seasonal peaks, troughs, and breadth) for both ILI and AGE in CCCs mirrored those reported at county and state levels, respectively. Two distinguishing features of CCC ILI outbreaks were noted in all three seasons: MCRISP ILI rates remained elevated after MDSS influenza counts abated, and MCRISP rates consistently peaked prior to MDSS influenza peaks. Neither of these phenomena were observed in comparing AGE outbreaks between surveillance systems.Discussion:ILI and AGE incidence rates from the MCRISP network appeared to broadly mirror epidemics from the established state surveillance system. MCRISP may act as a sentinel system for larger community outbreaks of respiratory disease.

Evaluation of the early warning, alert and response system after Cyclone Winston, Fiji, 2016.

To assess the performance of an early warning, alert and response system (EWARS) developed by the World Health Organization (WHO) - EWARS in a Box - that was used to detect and control disease outbreaks after Cyclone Winston caused destruction in Fiji on 20February 2016. Immediately after the cyclone, Fiji's Ministry of Health and Medical Services, supported by WHO, started to implement EWARS in a Box, which is a smartphone-based, automated, early warning surveillance system for rapid deployment during health emergencies. Both indicator-based and event-based surveillance were employed. The performance of the system between 7March and 29May 2016 was evaluated. Users' experience with the system was assessed in interviews using a semi-structured questionnaire and by a cross-sectional survey. The system's performance was assessed using data from the EWARS database. Indicator-based surveillance recorded 34113 cases of the nine syndromes under surveillance among 326861 consultations. Three confirmed outbreaks were detected, and no large outbreak was missed. Users were satisfied with the performance of EWARS and judged it useful for timely monitoring of disease trends and outbreak detection. The system was simple, stable and flexible and could be rapidly deployed during a health emergency. The automated collation, analysis and dissemination of data reduced the burden on surveillance teams, saved human resources, minimized human error and ensured teams could focus on public health responses. In Fiji, EWARS in a Box was effective in strengthening disease surveillance during a national emergency and was well regarded by users.

A modified CUSUM test to control postoutbreak false alarms.

The cumulative sum (CUSUM) control chart is a method for detecting whether the mean of a time series process has shifted beyond some tolerance (ie, is out of control). Originally developed in an industrial process control setting, the CUSUM statistic is typically reset to zero once a process is discovered to be out of control since the industrial process is then recalibrated to be in control. The CUSUM method is also used to detect disease outbreaks in prospective disease surveillance, with a disease outbreak coinciding with an out-of-control process. In a disease surveillance setting, resetting the CUSUM statistic is unrealistic, and a nonrestarting CUSUM chart is used instead. In practice, the nonrestarting CUSUM provides more information but suffers from a high false alarm rate following the end of an outbreak. In this paper, we propose a modified hypothesis test for use with the nonrestarting CUSUM when testing whether a process is out of control. By simulating statistics conditional on the presence of an out-of-control process in recent time periods, we are able to retain the CUSUM's power to detect an out-of-control process while controlling the post-out-of-control false alarm rate at the desired level. We demonstrate this method using data on a Salmonella Newport outbreak that occurred in Germany in 2011. We find that in 7 out of 8 states where the outbreak was detected, the outbreak was detected at the same speed as an unmodified nonrestarting CUSUM while controlling the postoutbreak rate of false alarms at the desired level.

Spatial analysis and temporal trends of antimicrobial use in Danish pig herds following the introduction of the Yellow Card scheme

Spatial analysis and temporal trends of antimicrobial use in Danish pig herds following the introduction of the Yellow Card scheme

Rapid response to meningococcal disease cluster in Foya district, Lofa County, Liberia January to February 2018.

IntroductionEarly detection of disease outbreaks is paramount to averting associated morbidity and mortality. In January 2018, nine cases including four deaths associated with meningococcal disease were reported in three communities of Foya district, Lofa County, Liberia. Due to the porous borders between Lofa County and communities in neighboring Sierra Leone and Guinea, the possibility of epidemic spread of meningococcal disease could not be underestimated.MethodsThe county incidence management system (IMS) was activated that coordinated the response activities. Daily meetings were conducted to review response activities progress and challenges. The district rapid response team (DRRT) was the frontline responders. The case based investigation form; case line list and contacts list were used for data collection. A data base was established and analysed daily for action. Tablets Ciprofloxacin were given for chemoprophylaxis.ResultsSixty-seven percent (67%) of the cases were males and also 67% of the affected age range was 3 to 14 years and attending primary school. The attack rate was 7/1,000 population and case fatality rate was 44.4 % with majority of the deaths occurring within 24-48 hours of symptoms onset. Three of the cases tested positive for Neisseria Meningitidis sero-type W while six cases were Epi-linked. None of the cases had recent meningococcal vaccination and no health-worker infections were registered.ConclusionThis cluster of cases of meningococcal disease during the meningitis season in a country that is not traditionally part of the meningitis belt emphasized the need for strengthening surveillance, preparedness and response capacity to meningitis.