Transmission Rate and Reproductive Number of the H5N1 Highly Pathogenic Avian Influenza Virus During the December 2005-July 2008 Epidemic in Nigeria

Abstract

We quantified the between-village transmission rate, β (the rate of transmission of H5N1 HPAI virus per effective contact), and the reproductive number, Re (the average number of outbreaks caused by one infectious village during its entire infectious period), of H5N1 highly pathogenic avian influenza (HPAI) virus in Nigeria using outbreak data collected between December 2005 and July 2008. We classified the outbreaks into two phases to assess the effectiveness of the control measures implemented. Phase 1 (December 2005-October 2006) represents the period when the Federal Government of Nigeria managed the HPAI surveillance and response measures, while Phase 2 (November 2006-July 2008) represents the time during which the Nigeria Avian Influenza Control and Human Pandemic Preparedness project (NAICP), funded by a World Bank credit of US$ 50 million, had taken over the management of most of the interventions. We used a total of 204 outbreaks from 176 villages that occurred in 78 local government areas of 25 states. The compartmental susceptible-infectious model was used as the analytical tool. Means and 95% percentile confidence intervals were obtained using bootstrapping techniques. The overall mean β (assuming a duration of infectiousness, T, of 12 days) was 0.07/day (95% percentile confidence interval: 0.06-0.09). The first and second phases of the epidemic had comparable β estimates of 0.06/day (0.04-0.09) and 0.08/day (0.06-0.10), respectively. The Re of the virus associated with these β and T estimates was 0.9 (0.7-1.1); the first and second phases of the epidemic had Re of 0.84 (0.5-1.2) and 0.9 (0.6-1.2), respectively. We conclude that the intervention measures implemented in the second phase of the epidemic had comparable effects to those implemented during the first phase and that the Re of the epidemic was low, indicating that the Nigeria H5N1 HPAI epidemic was unstable.

R0 Estimation for the African Swine Fever Epidemics in Wild Boar of Czech Republic and Belgium

“…However, for diseases with a high case-lethality ratio, mortality cases can be used as a proxy for the number of newly infected individuals (e.g., [11]). Once appropriate data on carcasses are collected, several mathematical methods can be used to estimate R 0 value [12][13][14]. When the value of R 0 is obtained it can be used to estimate the expected number of carcasses to find within each cycle of infection; the latter is summarised by the doubling time (time needed by the disease to duplicate the number of infected individuals).…”

Estimating the Basic Reproductive Number (R0) for African Swine Fever Virus (ASFV) Transmission between Pig Herds in Uganda

“…R 0 is the average number of secondary cases arising from a single infectious individual in a wholly susceptible population throughout its infectious period [ 18 – 23 ]. This parameter can be estimated using a variety of mathematical techniques [ 17 , 20 , 24 ]. This estimate provides a means to better understand the dynamics of infectious disease outbreaks and to assess the potential efficacy of disease control measures [ 25 ].…”

Estimating the between-farm transmission rates for highly pathogenic avian influenza subtype H5N1 epidemics in Bangladesh between 2007 and 2013

“…In a study based on 33 outbreaks in West Bengal, India during the period between 2008 and 2010, Pandit, Bunn, Pande, and Aly () estimated R 0 that ranged from 0.859 to 1.069 and Bett et al. () found a between‐village R 0 ranging from 0.7 to 1.1 for an epidemic that occurred in Nigeria.…”

“…Examples on the use of outbreak data to infer about HPAI epidemics include the estimation of per contact transmission probabilities of the virus between farms (Ssematimba, Elbers, Hagenaars, & de Jong, 2012), the assessment of the impact of intervention strategies in controlling outbreaks (Bett et al, 2014;Stegeman, Bouma, & de Jong, 2010;Stegeman et al, 2004;Walker et al, 2015) and general inference about epidemic-specific characteristics (Bavinck et al, 2009;Bos et al, 2009;Stegeman et al, 2004).…”

“…Marquetoux et al (2012) estimated a between-subdistrict R 0 varying between 1.27 and 1.60 in Thailand using 1208 outbreaks that occurred between July 2004 and April 2005. In a study based on 33 outbreaks in West Bengal, India during the period between 2008and 2010,Pandit, Bunn, Pande, and Aly (2013) estimated R 0 that ranged from 0.859 to 1.069 andBett et al (2014) found a betweenvillage R 0 ranging from 0.7 to 1.1 for an epidemic that occurred in Nigeria.…”