Potential risk zones and climatic factors influencing the occurrence and persistence of avian influenza viruses in the environment of live bird markets in Bangladesh

Abstract

Live bird markets (LBMs) are critical for poultry trade in many developing countries that are regarded as hotspots for the prevalence and contamination of avian influenza viruses (AIV). Therefore, we conducted weekly longitudinal environmental surveillance in LBMs to determine annual cyclic patterns of AIV subtypes, environmental risk zones, and the role of climatic factors on the AIV presence and persistence in the environment of LBM in Bangladesh. From January 2018 to March 2020, we collected weekly fecal and offal swab samples from each LBM and tested using rRT-PCR for the M gene and subtyped for H5, H7, and H9. We used Generalized Estimating Equations (GEE) approaches to account for repeated observations over time to correlate the AIV prevalence and potential risk factors and the negative binomial and Poisson model to investigate the role of climatic factors on environmental contamination of AIV at the LBM. Over the study period, 37.8% of samples tested AIV positive, 18.8% for A/H5, and A/H9 was, for 15.4%. We found the circulation of H5, H9, and co-circulation of H5 and H9 in the environmental surfaces year-round. The Generalized Estimating Equations (GEE) model reveals a distinct seasonal pattern in transmitting AIV and H5. Specifically, certain summer months exhibited a substantial reduction of risk up to 70-90% and 93-94% for AIV and H5 contamination, respectively. The slaughtering zone showed a significantly higher risk of contamination with H5, with a three-fold increase in risk compared to bird-holding zones. From the negative binomial model, we found that climatic factors like temperature and relative humidity were also significantly associated with weekly AIV circulation. An increase in temperature and relative humidity decreases the risk of AIV circulation. Our study underscores the significance of longitudinal environmental surveillance for identifying potential risk zones to detect H5 and H9 virus co-circulation and seasonal transmission, as well as the imperative for immediate interventions to reduce AIV at LBMs in Bangladesh. We recommend adopting a One Health approach to integrated AIV surveillance across animal, human, and environmental interfaces in order to prevent the epidemic and pandemic of AIV.

Keywords: Contaminations; H9; HPAI H5; Meteorological factors; One health; Slaughtering zone; Surveillance; Zoonotic spillover.

Fig. 1

Proportion of AIV subtypes weekly during 2018–2020. Each column comprises a week. The prevalence of each subtype each week is staked over each other where red bars indicate the proportion of A/H5 positive, blue bars A/H9 positive, yellow bars A/H5/H9 positive, and purple bars A/untype positive in each week. The dotted lines indicate the transition between years. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Proportion of confirmed AIV subtypes each month during 2018–2020. The prevalence of each subtype each month is staked over each other where red bars indicate the proportion of A/H5 positive, blue bars A/H9 positive, yellow bars A/H5/H9 positive, and purple bars A/Untype positive in each month. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

(A) AIV prevalence in LBM across the annual cycle. (B) A/H5 prevalence in LBM the yearly cycle. (C) A/H9 prevalence in LBM across the annual cycle. Each plot's bars represent the prevalence for that particular month and include a 95% CI. The blue colored bars in each figure denote months that fall into the winter season, while the gray colored bars indicate months that fall into the summer season. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Prevalence and 95% CI of AIV, A/H5, and A/H9 across the business type of market.

Fig. 5

Prevalence and 95% CI of AIV, A/H5, and A/H9 across the risk zones of the market.

Fig. 6

Prevalence and 95% CI of AIV, A/H5, and A/H9 across the sampling efforts.

Fig. 7

Odds ratios of the presence of AIV, H5, and H9 as compared to reference category of each independent variable (Intercept, reference category not shown) with 95% confidence intervals and significance stars (*) from the GEE model is plotted. The “neutral” dotted line, i.e., the vertical intercept, indicates no effect (x-axis position 1 for Odds ratio).

Fig. 8

Time series decomposition of weekly positive A/H5 count.

Fig. 9

(A) Wavelet coherence plot for A/H5 vs. Minimum temperature. (B) Wavelet Coherence plot for A/H5 vs. Maximum temperature. A colour spectrum indicates wavelet coherence. Red indicates high coherence, and blue indicates weak coherence as a function of the week of the study period (x-axis) and the oscillatory period (y-axis). Black lines indicate areas of coherence at a 5% significance level. Shaded areas represent regions where computed power spectra are less accurate due to boundary effects. Arrows pointing to the right mean that the variables are in phase. Arrows pointing to the left in our Figure indicate that the variables are out of phase. Downward arrows signify that the climate factor leads, while upward arrows indicate that the influenza virus leads in terms of their timing or influence. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 10

(A) Wavelet coherence plot for A/H5 vs. Humidity. (B) Wavelet Coherence plot for A/H5 vs. Rainfall. (C) Wavelet Coherence plot for A/H5 vs Wind speed. A colour spectrum indicates wavelet coherence. Red indicates high coherence, and blue indicates weak coherence as a function of the week of the study period (x-axis) and the oscillatory period (y-axis). Black lines indicate areas of coherence at a 5% significance level. Shaded areas represent regions where computed power spectra are less accurate due to boundary effects. Arrows pointing to the right mean that the variables are in phase. Arrows pointing to the left in our Figure indicate that the variables are out of phase. Downward arrows signify that the climate factor leads, while upward arrows indicate that the influenza virus leads in terms of their timing or influence. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 11

In panel A-C, relationships of Maximum temperature, relative humidity, and wind speed with A/H5 circulation are depicted, using the partial residuals of the response variables and depicting the marginal effect response curve for each relationship.

Fig. 12

In panel A-B, relationships of Minimum temperature and Rainfall with A/H5 circulation are depicted, using the partial residuals of the response variables and depicting the marginal effect response curve for each connection.