A Citizen Observatory Approach for Developing a Disease Outbreak Early Warning System

In health matters, early warning systems are timely surveillance systems that collect information on epidemic-prone diseases to trigger prompt public health interventions. However, these systems rarely apply statistical methods to detect changes in trends or sentinel events that would require intervention. Often, they rely on an in-depth review done by epidemiologists of the data coming in, which is rarely done systematically. This research introduced the use of ICT for collecting and analyzing citizen observations on disease trends and outbreaks. A citizen observatory ICT tool, which utilizes mobile and web features was developed. Data was collected on symptoms observed from diseases in four locations within Nairobi city. The system made use of mathematical models and outlier detection techniques to detect observations that deviated from the expected pattern in the dataset. New clusters were considered as outliers and the system flagged them as potential outbreaks. We clustered data using a K-Means algorithm and the Euclidean distance of each object from its corresponding cluster centre was obtained. From the results, the developed prototype was able to detect an outbreak of Flu and URTI diseases for the period of study. The proposed tool can therefore enhance the management of risks associated with disease outbreaks.  Keywords: Early Warning Systems, citizen observatory, health surveillance, Outlier Detection, Modeling disease outbreak

Response to malaria epidemics in Africa.

Malaria epidemics affect nonimmune populations in many highland and semi-arid areas of Africa. Effective prevention of these epidemics is challenging, particularly in the highlands where predictive accuracy of indicators is not sufficiently high to allow decisions involving expensive measures such as indoor residual spraying of insecticides. Advances in geographic information systems have proved useful in stratification of areas to guide selective targeting of interventions, including barrier application of insecticides in transmission foci to prevent spread of infection. Because rainfall is associated with epidemics in semi-arid areas, early warning methods based on seasonal climate predictions have been proposed. For most areas, response measures should focus on early recognition of anomalies and rapid mass drug administration. Vector control measures are useful if abnormal transmission is highly likely and if they can be selectively implemented at the early stages of an outbreak

Early warning signals in plant disease outbreaks

Infectious disease outbreaks in plants threaten ecosystems, agricultural crops and food trade. Currently, several fungal diseases are affecting forests worldwide, posing a major risk to tree species, habitats and consequently ecosystem decay. Prediction and control of disease spread are difficult, mainly due to the complexity of the interaction between individual components involved. In this work, we introduce a lattice-based epidemic model coupled with a stochastic process that mimics, in a very simplified way, the interaction between the hosts and pathogen. We studied the disease spread by measuring the propagation velocity of the pathogen on the susceptible hosts. Our quantitative results indicate the occurrence of a critical transition between two stable phases: local confinement and an extended epiphytotic outbreak that depends on the density of the susceptible individuals. Quantitative predictions of epiphytotics are performed using the framework early-warning indicators for impending regime shifts, widely applied on dynamical systems. These signals forecast successfully the outcome of the critical shift between the two stable phases before the system enters the epiphytotic regime. Our study demonstrates that early-warning indicators could be useful for the prediction of forest disease epidemics through mathematical and computational models suited to more specific pathogen–host-environmental interactions. Our results may also be useful to identify a suitable planting density to slow down disease spread and in the future, design highly resilient forests

The effect of distance on observed mortality, childhood pneumonia and vaccine efficacy in rural Gambia.

We investigated whether straight-line distance from residential compounds to healthcare facilities influenced mortality, the incidence of pneumonia and vaccine efficacy against pneumonia in rural Gambia. Clinical surveillance for pneumonia was conducted on 6938 children living in the catchment areas of the two largest healthcare facilities. Deaths were monitored by three-monthly home visits. Children living >5 km from the two largest healthcare facilities had a 2·78 [95% confidence interval (CI) 1·74-4·43] times higher risk of all-cause mortality compared to children living within 2 km of these facilities. The observed rate of clinical and radiological pneumonia was lower in children living >5 km from these facilities compared to those living within 2 km [rate ratios 0·65 (95% CI 0·57-0·73) and 0·74 (95% CI 0·55-0·98), respectively]. There was no association between distance and estimated pneumococcal vaccine efficacy. Geographical access to healthcare services is an important determinant of survival and pneumonia in children in rural Gambia

Event-based surveillance during EXPO Milan 2015. Rationale, tools, procedures, and initial results

More than 21 million participants attended EXPO Milan from May to October 2015, making it one of the largest protracted mass gathering events in Europe. Given the expected national and international population movement and health security issues associated with this event, Italy fully implemented, for the first time, an event-based surveillance (EBS) system focusing on naturally occurring infectious diseases and the monitoring of biological agents with potential for intentional release. The system started its pilot phase in March 2015 and was fully operational between April and November 2015. In order to set the specific objectives of the EBS system, and its complementary role to indicator-based surveillance, we defined a list of priority diseases and conditions. This list was designed on the basis of the probability and possible public health impact of infectious disease transmission, existing statutory surveillance systems in place, and any surveillance enhancements during the mass gathering event. This article reports the methodology used to design the EBS system for EXPO Milan and the results of 8 months of surveillance