Models of highly pathogenic avian influenza epidemics in commercial poultry flocks in Nigeria and Ghana

Pelletier, Sky T. K.; Rorres, Chris; Macko, Peter C.; Peters, Sarah T.; Smith, Gary

doi:10.1007/s11250-012-0124-2

Cited by 6 publications

(24 citation statements)

References 20 publications

(24 reference statements)

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“…A hot-spot analysis indicated that LGAs with a similar number of positive model simulations were clustered together and that the locations of 2006–2007 epidemics were generally within statistically “hot” zones while “cold” zones contained fewer or no epidemic events. While our model results match the smoothed incidence risk of H5N1 during the outbreak from 2005–2008 compiled by Henning et al [ 14 ], as well as the risk of Nigerian states calculated by Pelletier et al [ 50 ], we show slightly less risk associated with northern Nigeria. This may be due the fact that the outbreaks were first concentrated in the northern zones of Nigeria during the first phase of the epidemic [ 21 ] and thus, was likely an initial introduction site.…”

Section: Discussionsupporting

confidence: 84%

“…This may be due the fact that the outbreaks were first concentrated in the northern zones of Nigeria during the first phase of the epidemic [ 21 ] and thus, was likely an initial introduction site. Pelletier et al [ 50 ] demonstrated that location of the introduction of H5N1 into Nigeria greatly impacts the spread of a disease outbreak. As Pelletier et al [ 50 ] point out with H5N1 in Nigeria; transmission is a stochastic process and not all introductions lead to major epidemics.…”

Section: Discussionmentioning

confidence: 99%

“…Pelletier et al [ 50 ] demonstrated that location of the introduction of H5N1 into Nigeria greatly impacts the spread of a disease outbreak. As Pelletier et al [ 50 ] point out with H5N1 in Nigeria; transmission is a stochastic process and not all introductions lead to major epidemics. This comparison of our theoretical simulation model with an actual outbreak is an important validation step for the model, indicating that the model captures enough of the variation and important parameters in the real system to be useful for designing surveillance.…”

Section: Discussionmentioning

confidence: 99%

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Constructing Rigorous and Broad Biosurveillance Networks for Detecting Emerging Zoonotic Outbreaks

et al. 2015

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Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.

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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Constructing Rigorous and Broad Biosurveillance Networks for Detecting Emerging Zoonotic Outbreaks

et al. 2015

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“…The actions taken during outbreaks included notification of outbreaks to OIE, demarcation of culling and surveillance areas, culling of birds in the outbreak areas, an absolute ban on movement of poultry and products from the culling and surveillance zones, disposal of dead birds, instant compensation for culling, clean-up and disinfection, and post-operation surveillance [ 63 , 84 ]. After the outbreaks in 2006, active surveillance systems were implemented in wild birds, domestic poultry, and the human population throughout both countries [ 63 , 82 , [85] , [86] , [87] , [88] ].…”

Section: Discussion: the Dynamics Of Intersectoral Collaborationsmentioning

confidence: 99%

Intersectoral collaboration shaping One Health in the policy agenda: A comparative analysis of Ghana and India

et al. 2021

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Intersectoral collaborations are an integral component of the prevention and control of diseases in a complex health system. On the one hand, One Health (OH) is promoting the establishment of intersectoral collaborations for prevention at the human-animal-environment interface. On the other hand, operationalising OH can only be realized through intersectoral collaborations. This work contributes to broadening the knowledge of the process for operationalising OH by analysing the governance structures behind different initiatives that tackle health problems at the human-animal-environment interface. The cases taken as examples for the analysis are the control and response to rabies and avian influenza under “classical OH”, and the management of floods and droughts for insights into “extended OH”. Data from Ghana and India were collected and compared to identify the key elements that enable ISC for OH. Despite the case studies being heterogeneous in terms of their geographic, economic, social, cultural, and historical contexts, strong similarities were identified on how intersectoral collaborations in OH were initiated, managed, and taken to scale. The actions documented for rabies prevention and control were historically based on one sector being the leader and implementer of activities, while avian influenza management relied more on intersectoral collaborations with clearly defined sectoral responsibilities. The management of the impact of flood and droughts on health provided a good example of intersectoral collaborations achieved by sectoral integration; however, the human health component was only involved in the response stage in the case of Ghana, while for India, there were broader schemes of intersectoral collaborations for prevention, adaptation, and response concerning climate change and disaster.

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“…While the case-control literature is sometimes used to justify the assumptions that qualitatively underpin architecture of mathematical models of infectious disease transmission (e.g. Pelletier et al., 2012; Rorres et al, 2011), I have been unable to find any examples in which the actual value of the odds ratios measured during case-control studies have been used directly to estimate model parameters.…”

Section: Discussionmentioning

confidence: 99%

Food- and water-borne disease: Using case control studies to estimate the force of infection that accounts for primary, sporadic cases

Smith

2013

Epidemics

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Disease models which take explicit account of heterogeneities in the risk of infection offer significant advantages over models in which the risk of infection is assumed to be uniform across all hosts. However, estimating the incidence rate (force of infection) in the different at-risk (exposure) groups is no easy matter. Classically, epidemiologists differentiate groups of hosts with different infection-risks according to their exposure to putative explanatory risk factors. The importance of these risk factors is assessed by case-control studies, in which the measure of effect (the difference in disease occurrence between one population and another) is the odds ratio. This paper describes for the first time how- and under what circumstances – the incidence in these different exposure groups can be estimated from odds ratios derived from case control studies in which controls have been selected by density sampling. This new estimation technique can be applied to any transmission modality but is especially useful in the case of models of food- and water-borne disease for which the case control literature represents a vast and, as yet, untapped resource. The paper finishes with a worked example using one of the most common of all food- and water-borne pathogens, Toxoplasma gondii.

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Models of highly pathogenic avian influenza epidemics in commercial poultry flocks in Nigeria and Ghana

Cited by 6 publications

References 20 publications

Constructing Rigorous and Broad Biosurveillance Networks for Detecting Emerging Zoonotic Outbreaks

Constructing Rigorous and Broad Biosurveillance Networks for Detecting Emerging Zoonotic Outbreaks

Intersectoral collaboration shaping One Health in the policy agenda: A comparative analysis of Ghana and India

Food- and water-borne disease: Using case control studies to estimate the force of infection that accounts for primary, sporadic cases

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