Huanglongbing (HLB) is a devastating citrus disease worldwide. A three-pronged approach to controlling HLB has been suggested, namely, removal of HLB-symptomatic trees, psyllid control, and replacement with HLB-free trees. However, such a strategy did not lead to successful HLB control in many citrus producing regions. We hypothesize this is because of the small-scale or incomprehensive implementation of the program, conversely, a comprehensive implementation of such a strategy at regional level can successfully control HLB. Here we investigated the effects of region-wide comprehensive implementation of this scheme to control HLB in Gannan region, China, with a total planted citrus acreage of over 110,000 ha from 2013–2019. With the region-wide implementation of comprehensive HLB management, overall HLB incidence in Gannan decreased from 19.71% in 2014 to 3.86% in 2019. A partial implementation of such a program (without a comprehensive inoculum removal) at the regional level in Brazil resulted in HLB incidence increasing from 1.89% in 2010 to 19.02% in 2019. A dynamic regression model analyses predicated that in a region-wide comprehensive implementation of such a program, HLB incidence would be controlled to a level of less than 1%. Economic feasibility analyses showed that average net profits were positive for groves that implemented the comprehensive strategy, but negative for groves without such a program over a ten-year period. Overall, the key for the three-pronged program to successfully control HLB control is the large scale (region-wide) and comprehensiveness in implementation. This study provides valuable information to control HLB and other endemic diseases worldwide.

Purpose This paper proposes a new approach, Variational Temporal Deconfounder (VTD), for estimating individualized treatment effects (ITE) from longitudinal observational data, where we address the hidden confounding issues by using proxies (i.e., surrogate variables that serve for unobservable variables). Methods We build VTD by incorporating a variational recurrent autoencoder that learns the latent encodings of hidden confounders from observed proxies and an ITE estimation network that takes the learned hidden encodings to predict the probability of receiving treatments and potential outcomes. Results We test VTD on both synthetic and real-world clinical data, and the results from synthetic data experiments demonstrate VTD's effectiveness in deconfounding by outperforming existing methods, while results from two real-world datasets (i.e., Medical Information Mart for Intensive Care version III [MIMIC-III] and the National Alzheimer’s Coordinating Center [NACC] database) suggest that the performance of the VTD model outperforms existing baseline models, however, varies depending on the assumptions of underlying causal structures and availability of proxies for hidden confounders. Conclusion The VTD offers a unique solution to address the confounding bias without the "unconfoundedness" assumption when estimating the ITE from longitudinal observational data. The elimination of the requirement for the "unconfoundedness" assumption makes the VTD more versatile and practical in real-world clinical applications of personalized medicine.