Spatio-temporal epidemiology of Japanese encephalitis virus infection in pig populations of eastern Uttar Pradesh, India, 2013–2022

IF 2.4 2区 农林科学 Q3 INFECTIOUS DISEASES Zoonoses and Public Health Pub Date : 2024-03-14 DOI:10.1111/zph.13123
Himani Dhanze, Balbir B. Singh, Michael Walsh, M. Suman Kumar, Amit Kumar, Kiran N. Bhilegaonkar, Victoria J. Brookes
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Abstract

Aims

Japanese encephalitis (JE) is endemic in India. Although pigs are considered important hosts and sentinels for JE outbreaks in people, limited information is available on JE virus (JEV) surveillance in pigs.

Methods and Results

We investigated the spatio-temporal distribution of JEV seroprevalence and its association with climate variables in 4451 samples from pigs in 10 districts of eastern Uttar Pradesh, India, over 10 years from 2013 to 2022. The mean seroprevalence of IgG (2013–2022) and IgM (2017–2022) was 14% (95% CI 12.8–15.2) and 10.98% (95% CI 9.8–12.2), respectively. Throughout the region, higher seroprevalence from 2013 to 2017 was observed and was highly variable with no predictable spatio-temporal pattern between districts. Seroprevalence of up to 60.8% in Sant Kabir Nagar in 2016 and 69.5% in Gorakhpur district in 2017 for IgG and IgM was observed, respectively. IgG seroprevalence did not increase with age. Monthly time-series decomposition of IgG and IgM seroprevalence demonstrated annual cyclicity (3–4 peaks) with seasonality (higher, broader peaks in the summer and monsoon periods). However, most variance was due to the overall trend and the random components of the time series. Autoregressive time-series modelling of pigs sampled from Gorakhpur was insufficiently predictive for forecasting; however, an inverse association between humidity (but not rainfall or temperature) was observed.

Conclusions

Detection patterns confirm seasonal epidemic periods within year-round endemicity in pigs in eastern Uttar Pradesh. Lack of increasing age-associated seroprevalence indicates that JEV might not be immunizing in pigs which needs further investigation because models that inform public health interventions for JEV could be inaccurate if assuming long-term immunity in pigs. Although pigs are considered sentinels for human outbreaks, sufficient timeliness using sero-surveillance in pigs to inform public health interventions to prevent JEV in people will require more nuanced modelling than seroprevalence and broad climate variables alone.

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2013-2022 年印度北方邦东部猪群日本脑炎病毒感染的时空流行病学。
目的:日本脑炎(JE)是印度的地方病。尽管猪被认为是日本脑炎疫情在人体内爆发的重要宿主和哨兵,但有关猪体内日本脑炎病毒(JEV)监测的信息却十分有限:我们调查了 2013 年至 2022 年 10 年间印度北方邦东部 10 个县 4451 份猪样本中 JEV 血清流行率的时空分布及其与气候变量的关系。IgG(2013-2022年)和IgM(2017-2022年)的平均血清流行率分别为14%(95% CI 12.8-15.2)和10.98%(95% CI 9.8-12.2)。在整个地区,2013 年至 2017 年的血清流行率较高,且各地区之间变化很大,没有可预测的时空模式。2016 年,Sant Kabir Nagar 的 IgG 和 IgM 血清流行率分别高达 60.8%;2017 年,Gorakhpur 地区的 IgG 和 IgM 血清流行率分别高达 69.5%。IgG血清流行率并未随年龄增长而增加。IgG和IgM血清流行率的月时间序列分解显示出年度周期性(3-4个峰值)和季节性(夏季和季风期峰值更高、更宽)。不过,大部分差异是由时间序列的总体趋势和随机成分造成的。对戈勒克布尔采样的猪进行自回归时间序列建模并不能充分预测疫情;但是,可以观察到湿度(而非降雨量或温度)与疫情之间存在反比关系:检测模式证实了北方邦东部猪群全年流行的季节性流行期。与年龄相关的血清流行率没有增加,这表明 JEV 可能不会对猪产生免疫作用,这需要进一步调查,因为如果假定猪具有长期免疫力,那么为 JEV 公共卫生干预措施提供信息的模型就可能不准确。尽管猪被认为是人类疫情爆发的哨兵,但要充分及时地利用猪血清监测来为预防人感染 JEV 的公共卫生干预措施提供信息,需要建立比血清流行率和广泛的气候变量更细致的模型。
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来源期刊
Zoonoses and Public Health
Zoonoses and Public Health 医学-传染病学
CiteScore
5.30
自引率
4.20%
发文量
115
审稿时长
6-12 weeks
期刊介绍: Zoonoses and Public Health brings together veterinary and human health researchers and policy-makers by providing a venue for publishing integrated and global approaches to zoonoses and public health. The Editors will consider papers that focus on timely collaborative and multi-disciplinary research in zoonoses and public health. This journal provides rapid publication of original papers, reviews, and potential discussion papers embracing this collaborative spirit. Papers should advance the scientific knowledge of the sources, transmission, prevention and control of zoonoses and be authored by scientists with expertise in areas such as microbiology, virology, parasitology and epidemiology. Articles that incorporate recent data into new methods, applications, or approaches (e.g. statistical modeling) which enhance public health are strongly encouraged.
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