Perspectives of Predictive Epidemiology and Early Warning Systems for Rift Valley Fever in Garissa, Kenya

 Nanyingi M O1,3, Muchemi G M1, Kiama S G2,Thumbi S M5,6 and Bett B4


1Department of Public Health, Pharmacology and Toxicology, Faculty of Veterinary Medicine, University of Nairobi PO BOX PO BOX 29053-0065 Nairobi, Kenya 2 Wangari Maathai Institute for Environmental Studies and Peace, College of Agriculture and Veterinary Science, University of Nairobi PO BOX 30197 Nairobi, Kenya 3 Colorado State University, Livestock-Climate Change Collaborative Research Support Program, CO 80523-1644,USA 4International Livestock Research Institute (ILRI), Naivasha Road, P.O. Box 30709 Nairobi 00100, Kenya 5Kenya Medical Research Institute, US Centres for Disease Control and Prevention, PO Box 1578 Kisumu 6 Paul G Allen Global Animal Health, PO Box 647090, Washington State University, Pullman WA 99164-7090,509-335-2489

Abstract

Rift Valley Fever (RVF) is an arthropod-borne viral zoonosis with a potential global threat to domestic animals and humans. Climate variability is recognized as one of the major drivers contributing RVF epidemics and epizootics that have been closely linked to cyclic occurrence of the warm phase of the El Niño southern oscillation (ENSO) phenomenon. Using retrospective reanalysis and cross sectional participatory approaches we evaluate the impacts of climate change on pastoral communities and outline their roles in community based early warning systems for RVF. We compare the spatiotemporal correlation of normalized difference vegetation index (NDVI) and Rainfall Estimate Differences as surrogate predictors of RVF outbreaks in Garissa over the past decade. A bivariate regression model to provide a month-ahead lead-time for earlier prediction of RVF is described. We also explore the recent RVF outbreaks linkage to other environmental conditions using long-term sentinel data collected on the field. The results indicate a significant correlation between elevated rainfall and NDVI (> 0.43) anomalies with recent RVF epidemics (P < 0.5). Persistent elevated rainfall and NDVI suggest that there is a likelihood of another RVF outbreak due to enhance vector competence. Given the nearly linear relationship between rainfall and NDVI it is thus possible to utilize these factors to examine and predict spatially and temporally RVF epidemics for effective surveillance with limited resources. This small-scale focal study will contribute to various existing predictive tools and present a good opportunity for preparedness and mitigation of RVF by local, national and international organizations involved in the prevention and control of RVF.

Key words: Rift Valley Fever, Climate change; Early Warning Systems; Garissa

Rift Valley Fever Early Warning Systems for Pandemic preparedness in Kenya

Dr.Nanyingi M O

Interdecadal and interannual climate variability has led to emergence and reemergence of zoonoses in Kenya. Pastoralists and small holder farmers are mostly affected due to their higher vulnerability and poor adaptive capacity; predisposed by lack of accurate predictive warning systems. The Global Information and Early Warning and Information System (GLEWS), FAO Emergency Prevention System (EMPRES), Famine Early Warning System (FEWSNET) and Malaria Early Warning System (MEWS) are existing but inadequate databases that rely on seasonal weather forecasts, governments’ meteorological data and geospatial mapping repositories to predict pandemics of climate sensitive diseases (CSD). In Kenya the Department of Veterinary Services (DVS) under the Epidemiology communications Office has established a community based early warning (CBEWS) contingency plan to monitor the convergence of risk factors that predispose to Rift Valley Fever (RVF) outbreaks and effectively communicate to stakeholders for early response in a 3-5 month lead period. This is supported by non-governmental partners’; it involves the vulnerable communities, educational and research institutions. Some of the impediments in tackling CSD are lack of understanding of the drivers of climate change by local experts, dysfunctional communication channels, financial constraints, Ethno-religious barriers, trans-boundary conflicts, poor government policies and lack of human capacity and equipment in designing predictive tools. The prioritized beneficiaries of climate risk detection and prediction information systems are the pastoral and vulnerable communities; however this may be utilized by research institutions, government and donors for planning. Sustainable planning and decision making tools to support the development of appropriate climate change adaptation and mitigation strategies have been achieved by conducting community sensitization, awareness raising in schools, continuous training of veterinary, medical and public health personnel. The Kenya Meteorological Department in conjunction with DVS are working on rejuvenating information and dissemination EWS that targets end users like vernacular radio stations to confer a sense of ownership in the final results. We are investigating the Spatio-temporal distribution and burden analysis of RVF in Garissa, Northern Kenya; using model-based approaches in prediction of future outbreaks in response to climate change. We envisage employing participatory approaches and hotspots risk mapping in estimating the economic impacts on livelihoods and develop RVF -EWS for public preparedness. A Kenyan Zoonotic disease department has been specifically established for technical capacity building to standardize the management of emerging zoonoses including CSD. Quasi real-time syndromic and sentinel e- surveillance using mobile phones involving communities is in experimental phase in RVF monitoring. Our dissemination of geographic, statistical and meteorological data for vulnerability and early warning platforms is hinged on collaboration of regional animal and human health institutions in managing of existing and emerging diseases of veterinary and public health significance. We take therefore cognizance of a community based approach in early and timely detection of RVF is vital to veterinary, health authorities and policy-makers in immediate decision making. It contributes to an integrated climate risk assessment of livestock vulnerability analysis using climate dependent RVF model to develop predictive risk maps that will be crucial in current and future control plans of other climate sensitive diseases and possibly provide Early Warning Systems (EWS). The output will contribute to institutional contingency frameworks dealing with concepts and indicators of warning systems which will facilitate the early identification of potential climate sensitive epidemics and decision support systems

Climate Change Vulnerability, Adaptation and Mitigation of Livestock Systems in Kenya

Nanyingi M O,1,2,3, Kiama S G,1, Thumbi S M'4 and Muchemi G M,2 1 Wangari Maathai Institute for Environmental Studies and Peace, College of Agriculture and Veterinary Science, University of Nairobi PO BOX 30197 Nairobi, Kenya 2 Department of Public Health, Pharmacology and Toxicology, Faculty of Veterinary Medicine, University of Nairobi PO BOX 30197 Nairobi, Kenya 3 Colorado State University Fort Collins, Livestock‐Climate Change Collaborative Research Support Program, CO 80523‐ 1644,USA 4 Centre for Immunity, Infection and Evolution, Kings Buildings ,University of Edinburgh, , West Mains Road, EH9 6JT, City of Edinburgh,Scotland,UK In Kenya, global circulation models predict that, by the year 2100, climate change will increase temperatures by about 4°C leading to massive crop failure, reduced availability of forage and water, livestock mortality and loss of livelihoods. Consequently vast economic impacts are expected, because of higher vulnerability and the exceeding magnitude of future hazards to the adaptive capacity of vulnerable communities. Historically livestock keepers have developed adaptive measures that include traditional early warning systems, use of emergency fodders, multi‐species composition of herds and nomadic mobility to reduce their vulnerability. However, lack of understanding of the drivers of climate change due to inconsistent weather data remains a major challenge causing unreliable and inaccurate prediction of climate change patterns. It is important to identify replicable and cost effective mitigation activities to strengthen the adaptive capacities to climate change of affected communities. Here we review strategies to bridge the knowledge gap in understanding the present and future impacts of climate change on indigenous livestock production systems and options of adaptation to and climate change mitigation based on the indigenous knowledge. We propose a systematic methodology to study vulnerability in the context of multiple stressors and the potential for utilization of participatory mapping tools, geographic data and predictive models of infectious disease burden for anticipatory and reactive adaptive preparedness. The overall thrust of the review is to improve the ability of vulnerable people and their livestock to be more resilient to current climate variability and their decision‐making to climate change. Key words: Climate change; Livestock; Adaptation; Kenya

Towards a Climate Change database for livestock adaptation and mitigation in Kenya

The Kenyan rural communities face a myriad of challenges including poverty, food security, scarcity of water, and challenges emerging due to global warming and climate change. Notable direct effects include higher temperatures and drastic changes in rainfall patterns, consequently aberrant transmission models, and increased spread of existing vector-borne diseases, emergence and re-emergence of infectious diseases. Major challenges for adaptation interventions in Kenya include insufficient local level historic and future climate change information. Given the complexity of livestock and crop-livestock systems, a mix of technological, policy and institutional innovations will inevitably be required. Here we propose approaches that can be used to develop reliable climate databases and to incorporate these data into predictive risk models. We hypothesize that techniques should be further refined to produce detailed relational databases. The proposed climate system models are to provide insights on climate variability and impacts on livestock, they are designated as problem-solving tools that allow users to process and analyse climate data in a multidisciplinary context. They should be ideal for storage, archiving, display, analysis and interpretation of the localised impacts, and the importance of identifying appropriate options that can help livestock keepers adapt to climate change. However we reckon the overarching issues of shrinking government budgets, curriculum suitability and need for collaboration to expand our knowledge of how climate change and increasing climate variability will affect livestock systems and the livelihoods of the people who depend on them.

Syndromic Early Warning Surveillance Networks in Kenya

Early detection is key to controlling disease outbreaks in the developing countries. In Kenya a small scale surveillance system is being established by a local network known as Khwisero Animal Health Surveillance Network (KAHSN), In Kakamega County of Western Kenya, KAHSN draws on the disease detection capabilities of practicing veterinarians, district and regional diagnostic laboratories and the national government.

The KAHSN bolsters Khwisero’s domestic early warning surveillance capacity for detecting new and emerging diseases of animals that could affect animal health, the food supply or public health. It brings together animal disease surveillance information from many sources - veterinary practitioners, veterinary diagnostic laboratories and animal health agencies - so that baselines can be established and trends identified. By amalgamating information gathered from surveys, syndromic surveillance and rumour reports from the field, tracking the on-going animal health status of the country will be improved.

The identification of aberrant trends of disease outbreaks prompts further investigation and assessment of situations. Since early detection is key to early control of any disease outbreak, the KAHSN is an important component of Kenyas’ defense against serious threats to animal and human health.

Animal disease surveillance supports Kenyas’ ability to recognize and deal with emerging animal disease problems. Surveillance also plays an import role in providing Kenyan livestock and poultry products access to more markets. Major notifiable diseases of livestock are routinely monitored and outbreaks reported in a established but laborious, if not erratic system.
Avian influenza (H1N1) is of major focus and its invariably undetected due to inadequate surveillance personell and laboratory facilities in the marginalized and rural areas. A collaboration between the Veterinary offices and regional research laboratories would enhance the detection and reporting of this(H1N1) and other Zoonotic infections.

The Khwisero District Veterinary Office is establishing a small scale model “Khwisero Animal Health Surveillance Network (KAHSN)” ; its collaborative network of animal health surveillance and diagnostic system to improve the capacity to detect emerging animal disease threats in real time. The KAHSN focuses particularly on those animal disease threats that could have zoonotic potential and provide a rapid response to minimize the human health and economic risks to Khwisero District.

The KAHSN combines surveillance data received from many sources and simultaneously alerts both human and animal health authorities in other jurisdictions within Western Kenya when potential animal disease threats are identified.

The key outputs of the KAHSN are:
• An early warning system for animal disease threats to the food supply, food safety or public health;
• A regional laboratory network for the rapid diagnosis of serious infectious animal diseases; and
• An information-sharing network linking national and international research agencies and departments of animal and human health

Features of the KAHSN
The model is set on open source softwares that enable suite of to collect, aggregate and visualize data, Mainly Open data Kit (ODK), will be utilised to aggreagate GIS data and mobile phone devices with The Android Emulators SDK, these successfuly ran a realtime data collection and synchronization to local internet servers. The use of interactive risk maps will enable faster emergency reactions in suspected outbreaks.

KAHSN will collaborate with the Khwisero Network for Public Health Intelligence (KNPHI) to establish rapid communication and identification of emerging animal and human disease issues. This newly established network increases the surge capacity of regional laboratories to rapidly diagnose serious and infectious animal diseases. By combining their diagnostic surge capacities with those of provincial and university laboratories that have diagnostic capabilities for foreign animal disease, the response to an outbreak is being enhanced. This establishes interoperability between laboratories by using common protocols and reagents. It also allows technical and scientific staff to exchange, participate in and share expertise.

Early detection of Zoonotic diseases by Syndromic Surveillance Networks (KAHSN)

Syndromic Surveillance Networks in Early detection of Zoonotic diseases

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