scholarly journals Interpreting the transmissibility of the avian influenza A(H7N9) infection from 2013 to 2015 in Zhejiang Province, China

2015 ◽  
Vol 144 (8) ◽  
pp. 1584-1591 ◽  
Author(s):  
K. C. CHONG ◽  
X. WANG ◽  
S. LIU ◽  
J. CAI ◽  
X. SU ◽  
...  
Keyword(s):  
Influenza A ◽  
Transmission Rate ◽  
Credible Interval ◽  
Zhejiang Province ◽  
Control Measures ◽  
Posterior Mean ◽  
Key Indicator ◽  
Intervention Measures ◽  
Live Poultry ◽  
H7n9 Infection

SUMMARYThree epidemic waves of human influenza A(H7N9) were documented in several different provinces in China between 2013 and 2015. With limited understanding of the potential for human-to-human transmission, it was difficult to implement control measures efficiently or to inform the public adequately about the application of interventions. In this study, the human-to-human transmission rate for the epidemics that occurred between 2013 and 2015 in Zhejiang Province, China, was analysed. The reproduction number (R), a key indicator of transmission intensity, was estimated by fitting the number of infections from poultry to humans and from humans to humans into a mathematical model. The posterior mean R for human-to-human transmission was estimated to be 0·27, with a 95% credible interval of 0·14–0·44 for the first wave, whereas the posterior mean Rs decreased to 0·15 in the second and third waves. Overall, these estimates indicate that a human H7N9 pandemic is unlikely to occur in Zhejiang. The reductions in the viral transmissibility and the number of poultry-transmitted infections after the first epidemic may be attributable to the various intervention measures taken, including changes in the extent of closures of live poultry markets.

scholarly journals Is Nigeria really on top of COVID-19? Message from effective reproduction number

2020 ◽  
Author(s):  
Adeshina Israel Adekunle ◽  
Oyelola Adegboye ◽  
Ezra Gayawan ◽  
Emma McBryde
Keyword(s):  
Public Health ◽  
Compartmental Model ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Credible Interval ◽  
Control Measures ◽  
Maximum Value ◽  
Intervention Measures ◽  
Death Cases ◽  
Epidemiological Parameters

Following the importation of Covid-19 into Nigeria on the 27 February 2020 and then the outbreak, the question is: how do we anticipate the progression of the ongoing epidemics following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of Covid-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R_0). This also enables us to estimate the daily transmission rate (β) that determines the effect of social distancing. We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on Covid-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 1.78. Most importantly, the R(t) is strictly greater than one from April 13 till 7 May 2020. The R_0 is estimated to be 2.42 with credible interval: (2.37, 2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below one. Also, the estimated fractional reported symptomatic cases are between 10 to 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

scholarly journals Changing geographic patterns and risk factors for avian influenza A(H7N9) infection in China

2017 ◽  
Author(s):  
Jean Artois ◽  
Xiling Wang ◽  
Hui Jiang ◽  
Ying Qin ◽  
Morgan Pearcy ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Range Expansion ◽  
Influenza A ◽  
Northern China ◽  
Incidence Rates ◽  
Control Measures ◽  
Live Poultry ◽  
H7n9 Infection ◽  
Prevention And Control Measures ◽  
And Control

AbstractThe 5th epidemic wave in 2016-2017 of avian influenza A(H7N9) virus in China caused more human cases than any previous waves but the factors that may explain the recent range expansion and surge in incidence remain unknown. We investigated the effect of anthropogenic, poultry and wetland information and of market closures on all epidemic waves (1-5). Poultry predictor variables recently became much more important than before, supporting the assumption of much wider H7N9 transmission in the chicken reservoir, that could be linked to increases in pathogenicity. We show that the future range expansion of H7N9 to northern China may translate into a higher risk of coinciding peaks with those of seasonal influenza, leading to a higher risk of reassortments. Live-poultry market closures are showed to be effective in reducing the local incidence rates of H7N9 human cases, but should be paired with other prevention and control measures to prevent transmission.

scholarly journals Is Nigeria really on top of COVID-19? Message from effective reproduction number

2020 ◽  
Vol 148 ◽  
Author(s):  
A. I. Adekunle ◽  
O. A. Adegboye ◽  
E. Gayawan ◽  
E. S. McBryde
Keyword(s):  
Public Health ◽  
Compartmental Model ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Credible Interval ◽  
Control Measures ◽  
Maximum Value ◽  
Intervention Measures ◽  
Death Cases ◽  
Epidemiological Parameters

Abstract Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) – this also enables us to estimate the initial daily transmission rate (β0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37–2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

scholarly journals Live Poultry Market Closure and Avian Influenza A (H7N9) infection in cities of China, 2013-2017: An ecological study

2020 ◽  
Author(s):  
Ying Chen ◽  
Jian Cheng ◽  
Zhiwei Xu ◽  
Wenbiao Hu ◽  
Jiahai Lu
Keyword(s):  
Avian Influenza ◽  
Risk Reduction ◽  
Influenza A ◽  
Reduction Rate ◽  
Dominant Factor ◽  
Effective Measure ◽  
Partial Closure ◽  
Live Poultry ◽  
H7n9 Infection ◽  
The Impact

Abstract Background Previous studies have proven that the closure of live poultry markets (LPMs) was an effective intervention to reduce human risk of avian influenza A (H7N9) infection, but evidence is limited on the impact of scale and duration of LPMs closure on the transmission of H7N9. Method Five cities (i.e., Shanghai, Suzhou, Shenzhen, Guangzhou and Hangzhou) with the largest number of H7N9 cases in mainland China from 2013-2017 were selected in this study. Data on laboratory-confirmed H7N9 human cases in those five cities were obtained from the Chinese National Influenza Centre. The detailed information of LPMs closure (i.e., area and duration) was obtained from the Ministry of Agriculture. We used a generalized linear model with a Poisson link to estimate the effect of LPMs closure, reported as relative risk reduction (RRR). We used classification and regression trees (CARTs) to select and quantify the dominant factor of H7N9 infection. Results All five cities implemented the LPMs closure, and the risk of H7N9 infection decreased significantly after LPMs closure with RRR ranging from 0.80-0.93. Respectively, a long-term LPMs closure for 10-13 weeks elicited a sustained and highly significant risk reduction of H7N9 infection (RRR = 0.98). Short-time LPMs closure with 2 weeks in every epidemic did not reduce the risk of H7N9 infection (p>0.05). Partially closed LPMs in some suburbs contributed only 35% for reduction rate (RRR=0.35). Shenzhen implemented partial closure for first 3 epidemics (p>0.05) and all closure in the latest 2 epidemic waves (RRR=0.64). Conclusion Our findings suggest that LPMs all closure in whole city can be a highly effective measure comparing with partial closure (i.e. only urban closure, suburb and country remain open). Extend the duration of closure and consider permanently closing the LPMs will help improve the control effect. The effect of LPMs closure is greater than that of meteorology on H7N9 transmission.

scholarly journals Live Poultry Market Closure and Avian Influenza A (H7N9) infection in cities of China, 2013-2017: An ecological study

2020 ◽  
Author(s):  
Ying Chen ◽  
Jian Cheng ◽  
Zhiwei Xu ◽  
Wenbiao Hu ◽  
Jiahai Lu
Keyword(s):  
Avian Influenza ◽  
Risk Reduction ◽  
Influenza A ◽  
Reduction Rate ◽  
Dominant Factor ◽  
Effective Measure ◽  
Partial Closure ◽  
Live Poultry ◽  
H7n9 Infection ◽  
The Impact

Abstract Background Previous studies have proven that the closure of live poultry markets (LPMs) was an effective intervention to reduce human risk of avian influenza A (H7N9) infection, but evidence is limited on the impact of scale and duration of LPMs closure on the transmission of H7N9. Method Five cities (i.e., Shanghai, Suzhou, Shenzhen, Guangzhou and Hangzhou) with the largest number of H7N9 cases in mainland China from 2013-2017 were selected in this study. Data on laboratory-confirmed H7N9 human cases in those five cities were obtained from the Chinese National Influenza Centre. The detailed information of LPMs closure (i.e., area and duration) was obtained from the Ministry of Agriculture. We used a generalized linear model with a Poisson link to estimate the effect of LPMs closure, reported as relative risk reduction (RRR). We used classification and regression trees (CARTs) to select and quantify the dominant factor of H7N9 infection. Results All five cities implemented the LPMs closure, and the risk of H7N9 infection decreased significantly after LPMs closure with RRR ranging from 0.80-0.93. Respectively, a long-term LPMs closure for 10-13 weeks elicited a sustained and highly significant risk reduction of H7N9 infection (RRR = 0.98). Short-time LPMs closure with 2 weeks in every epidemic did not reduce the risk of H7N9 infection (p>0.05). Partially closed LPMs in some suburbs contributed only 35% for reduction rate (RRR=0.35). Shenzhen implemented partial closure for first 3 epidemics (p>0.05) and all closure in the latest 2 epidemic waves (RRR=0.64). Conclusion Our findings suggest that LPMs all closure in whole city can be a highly effective measure comparing with partial closure (i.e. only urban closure, suburb and country remain open). Extend the duration of closure and consider permanently closing the LPMs will help improve the control effect. The effect of LPMs closure is greater than that of meteorology on H7N9 transmission.

scholarly journals Live poultry market closure and avian influenza A (H7N9) infection in cities of China, 2013–2017: an ecological study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ying Chen ◽  
Jian Cheng ◽  
Zhiwei Xu ◽  
Wenbiao Hu ◽  
Jiahai Lu
Keyword(s):  
Avian Influenza ◽  
Influenza A ◽  
Ecological Study ◽  
Live Poultry ◽  
H7n9 Infection

scholarly journals Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Marius Gilbert ◽  
Nick Golding ◽  
Hang Zhou ◽  
G. R. William Wint ◽  
Timothy P. Robinson ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Influenza A ◽  
Live Poultry ◽  
H7n9 Infection ◽  
Live Poultry Markets

Prognosis and survival of 128 patients with severe avian influenza A(H7N9) infection in Zhejiang province, China

2014 ◽  
Vol 143 (9) ◽  
pp. 1833-1838 ◽  
Author(s):  
Y. Y. XIAO ◽  
J. CAI ◽  
X. Y. WANG ◽  
F. D. LI ◽  
X. P. SHANG ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Hospital Admission ◽  
Odds Ratio ◽  
Influenza A ◽  
Mainland China ◽  
Zhejiang Province ◽  
Time Interval ◽  
Clinical Visit ◽  
Severe Influenza ◽  
H7n9 Infection

SUMMARYNo published studies have discussed details of the prognosis and survival of patients with severe avian influenza A(H7N9) infection. In this study we analysed 128 laboratory-confirmed cases of severe H7N9 infection in Zhejiang province, the most affected region during the H7N9 epidemic in mainland China. We found that an increase in patient age by 5 years was associated with a 1·41 [95% confidence interval (CI) 1·19–1·67] times odds ratio for fatality. In addition, the time interval between the first clinical visit after symptom onset and hospital admission was inversely associated with survival time since admission. Of the 47 patients who died of the disease, when the time interval between the first clinical visit and hospital admission increased by 1 day, the duration of survival was 0·78 times (95% CI 0·62–0·98) as long. Our results suggest that patients with severe influenza H7N9 infection at older ages were at a higher risk of fatality, and that a delay in hospital admission was associated with more rapid death. More studies are required to corroborate our major findings.

scholarly journals Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: a statistical analysis in Tunisia from February 29 to May 5, 2020

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Khouloud Talmoudi ◽  
Mouna Safer ◽  
Hejer Letaief ◽  
Aicha Hchaichi ◽  
Chahida Harizi ◽  
...  
Keyword(s):  
Credible Interval ◽  
Emerging Diseases ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Contact Tracing ◽  
Epidemic Threshold ◽  
Illness Onset ◽  
Intervention Measures ◽  
Temporal Reproduction ◽  
Serial Interval

Abstract Background Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29–May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results Four hundred ninety-one of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% Confidence Interval (CI) 4.66–5.95] and standard deviation 0.26 [95% CI 0.23–0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% Credible Interval (CrI) 2.73–3.69] to 1.77 [95% CrI 1.49–2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CrI 0.84–0.94]) by national lockdown measure. Conclusions Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

scholarly journals Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: A statistical analysis in Tunisia from February 29 to May 5, 2020 

2020 ◽  
Author(s):  
Khouloud Talmoudi ◽  
Mouna Safer ◽  
Hejer Letaief ◽  
Aicha Hchaichi ◽  
Chahida Harizi ◽  
...  
Keyword(s):  
Credible Interval ◽  
Emerging Diseases ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Contact Tracing ◽  
Epidemic Threshold ◽  
Illness Onset ◽  
Intervention Measures ◽  
Temporal Reproduction ◽  
Serial Interval

Abstract Background: Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods: We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results: 491 of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% Confidence Interval (CI) 4.66-5.95] and standard deviation 0.26 [95% CI 0.23-0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% Credible Interval (CrI) 2.73-3.69] to 1.77 [95% CrI 1.49-2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CrI 0.84-0.94]) by national lockdown measure.Conclusions: Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

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