Joint spatiotemporal modelling reveals seasonally dynamic patterns of Japanese encephalitis vector abundance across India.
Joint spatiotemporal modelling reveals seasonally dynamic patterns of Japanese encephalitis vector abundance across India.
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DOI：
10.1371/journal.pntd.0010218
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发表时间：
2022-03
影响因子：
3.8
通讯作者：
Jones KE
中科院分区：
文献类型：
--
作者：
Franklinos LHV;Redding DW;Lucas TCD;Gibb R;Abubakar I;Jones KE
预测媒介丰度和季节性是蚊媒疾病（MBD）危害的关键组成部分，对于确定MBD风险热点和有效的目标干预至关重要。日本脑炎（JE）是一种重要的MBD，是亚洲病毒性脑病的主要原因，估计每年有10万例病例，但缺乏关于主要媒介三带喙库蚊的数据。我们开发了一个贝叶斯联合似然模型，该模型结合了来自现有病媒发生和丰度数据的信息，以预测C。三带喙线虫（乙脑危害的一个组成部分）在印度，以及研究这些模式的环境驱动因素。利用24项研究中57个地点的数据整理，我们发现了受气候和土地利用因素影响的乙脑病媒丰度的不同季节和空间格局。滞后的降水量，温度和土地利用强度指标水稻作物种植的主要驱动力的病媒丰度，独立的季节，或空间变化。与代表静态危害预测的基线模型（MAE = 0.95）相比，纳入环境因素和季节项提高了模型预测准确性（随机交叉验证的平均绝对误差[MAE]= 0.48），表明季节性环境条件在预测乙脑病媒丰度方面的重要性。在印度各地，病媒丰度差异很大，预测北方、东北部、东部和南部地区的病媒丰度较高，尽管这包括季节性（例如，北方邦、西孟加拉）至多年生植物（例如，阿萨姆邦、泰米尔纳德邦）。滞后一个月的预测病媒丰度是乙脑爆发的一个重要预测因子（比值比2.45，95%置信区间：1.52-4.08），突出了病媒丰度作为乙脑危害代理的可能发展。我们展示了一种新的方法，利用稀疏的矢量监测数据的信息来预测季节性矢量丰度-乙脑危害的关键组成部分-在大的空间尺度上，为决策者提供更好的指导，有针对性的矢量监测和控制工作。日本脑炎（JE）是亚洲病毒性脑病的主要原因，估计每年有100，000例病例和25，000例死亡。然而，关于主要蚊子媒介三带喙库蚊（JE危害的关键组成部分）的数据不足，无法进行针对公共卫生干预措施所需的危害估计。以往的研究提供了有限的估计乙脑的危害，往往预测潜在的病媒发生的地理分布没有考虑到病媒丰度，季节性，或预测的不确定性。这项研究详细介绍了一种新的方法来预测时空模式，乙脑病媒丰度使用联合似然建模技术，利用稀疏矢量监测数据的信息。我们发现，乙脑病媒丰度的模式是由季节性和环境因素驱动的，因此证明了以前可用的静态病媒分布图在估计乙脑危害的病媒人口组成部分方面的局限性。滞后一个月的病媒丰度预测显示与乙脑爆发呈正相关，表明病媒丰度可能用作乙脑危害的代理。虽然病媒监测数据有限，但联合似然模型为病媒丰度预测提供了有用的信息。这项研究为决策者提供了关于乙脑病媒丰度分布的更完整的情况，可用于有针对性地开展病媒监测和控制工作，并加强资源分配。
Predicting vector abundance and seasonality, key components of mosquito-borne disease (MBD) hazard, is essential to determine hotspots of MBD risk and target interventions effectively. Japanese encephalitis (JE), an important MBD, is a leading cause of viral encephalopathy in Asia with 100,000 cases estimated annually, but data on the principal vector Culex tritaeniorhynchus is lacking. We developed a Bayesian joint-likelihood model that combined information from available vector occurrence and abundance data to predict seasonal vector abundance for C. tritaeniorhynchus (a constituent of JE hazard) across India, as well as examining the environmental drivers of these patterns. Using data collated from 57 locations from 24 studies, we find distinct seasonal and spatial patterns of JE vector abundance influenced by climatic and land use factors. Lagged precipitation, temperature and land use intensity metrics for rice crop cultivation were the main drivers of vector abundance, independent of seasonal, or spatial variation. The inclusion of environmental factors and a seasonal term improved model prediction accuracy (mean absolute error [MAE] for random cross validation = 0.48) compared to a baseline model representative of static hazard predictions (MAE = 0.95), signalling the importance of seasonal environmental conditions in predicting JE vector abundance. Vector abundance varied widely across India with high abundance predicted in northern, north-eastern, eastern, and southern regions, although this ranged from seasonal (e.g., Uttar Pradesh, West Bengal) to perennial (e.g., Assam, Tamil Nadu). One-month lagged predicted vector abundance was a significant predictor of JE outbreaks (odds ratio 2.45, 95% confidence interval: 1.52–4.08), highlighting the possible development of vector abundance as a proxy for JE hazard. We demonstrate a novel approach that leverages information from sparse vector surveillance data to predict seasonal vector abundance–a key component of JE hazard–over large spatial scales, providing decision-makers with better guidance for targeting vector surveillance and control efforts. Japanese encephalitis (JE) is the leading cause of viral encephalopathy in Asia with an estimated 100,000 annual cases and 25,000 deaths. However, insufficient data on the predominant mosquito vector Culex tritaeniorhynchus–a key component of JE hazard–precludes hazard estimation required to target public health interventions. Previous studies have provided limited estimates of JE hazard, often predicting geographic distributions of potential vector occurrence without accounting for vector abundance, seasonality, or uncertainty in predictions. This study details a novel approach to predict spatiotemporal patterns in JE vector abundance using a joint-likelihood modelling technique that leverages information from sparse vector surveillance data. We showed that patterns in JE vector abundance were driven by seasonality and environmental factors and so demonstrated the limitations of previously available static vector distribution maps in estimating the vector population component of JE hazard. One-month lagged vector abundance predictions showed a positive relationship with JE outbreaks, signalling the potential use of vector abundance as a proxy for JE hazard. While vector surveillance data are limited, joint-likelihood models offer a useful approach to inform vector abundance predictions. This study provides decision-makers with a more complete picture of the distribution of JE vector abundance and can be used to target vector surveillance and control efforts and enhance the allocation of resources.
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影响因子：
6.1
作者：
Hooten, M. B.;Hobbs, N. T.
通讯作者：
Hobbs, N. T.
影响因子：
9.8
作者：
Abatzoglou JT;Dobrowski SZ;Parks SA;Hegewisch KC
通讯作者：
Hegewisch KC
影响因子：
3
作者：
Baeza A;Bouma MJ;Dobson AP;Dhiman R;Srivastava HC;Pascual M
通讯作者：
Pascual M
影响因子：
4.6
作者：
Humphreys, John M.;Murrow, Jennifer L.;Prosser, Diann J.
通讯作者：
Prosser, Diann J.
影响因子：
3.2
作者：
Ewing, David A.;Purse, Bethan V.;White, Steven M.
通讯作者：
White, Steven M.