Background The highly infectious coronavirus disease (COVID-19) was first detected in Wuhan, China in December 2019 and subsequently spread to 212 countries and territories around the world, infecting millions of people. In India, a large country of about 1.3 billion people, the disease was first detected on January 30, 2020, in a student returning from Wuhan. The total number of confirmed infections in India as of May 3, 2020, is more than 37,000 and is currently growing fast. Objective Most of the prior research and media coverage focused on the number of infections in the entire country. However, given the size and diversity of India, it is important to look at the spread of the disease in each state separately, wherein the situations are quite different. In this paper, we aim to analyze data on the number of infected people in each Indian state (restricted to only those states with enough data for prediction) and predict the number of infections for that state in the next 30 days. We hope that such statewise predictions would help the state governments better channelize their limited health care resources. Methods Since predictions from any one model can potentially be misleading, we considered three growth models, namely, the logistic, the exponential, and the susceptible-infectious-susceptible models, and finally developed a data-driven ensemble of predictions from the logistic and the exponential models using functions of the model-free maximum daily infection rate (DIR) over the last 2 weeks (a measure of recent trend) as weights. The DIR is used to measure the success of the nationwide lockdown. We jointly interpreted the results from all models along with the recent DIR values for each state and categorized the states as severe, moderate, or controlled. Results We found that 7 states, namely, Maharashtra, Delhi, Gujarat, Madhya Pradesh, Andhra Pradesh, Uttar Pradesh, and West Bengal are in the severe category. Among the remaining states, Tamil Nadu, Rajasthan, Punjab, and Bihar are in the moderate category, whereas Kerala, Haryana, Jammu and Kashmir, Karnataka, and Telangana are in the controlled category. We also tabulated actual predicted numbers from various models for each state. All the R2 values corresponding to the logistic and the exponential models are above 0.90, indicating a reasonable goodness of fit. We also provide a web application to see the forecast based on recent data that is updated regularly. Conclusions States with nondecreasing DIR values need to immediately ramp up the preventive measures to combat the COVID-19 pandemic. On the other hand, the states with decreasing DIR can maintain the same status to see the DIR slowly become zero or negative for a consecutive 14 days to be able to declare the end of the pandemic.

Summary A dynamic treatment regimen consists of decision rules that recommend how to individualize treatment to patients based on available treatment and covariate history. In many scientific domains, these decision rules are shared across stages of intervention. As an illustrative example, we discuss STAR*D, a multistage randomized clinical trial for treating major depression. Estimating these shared decision rules often amounts to estimating parameters indexing the decision rules that are shared across stages. In this paper, we propose a novel simultaneous estimation procedure for the shared parameters based on Q-learning. We provide an extensive simulation study to illustrate the merit of the proposed method over simple competitors, in terms of the treatment allocation matching of the procedure with the “oracle” procedure, defined as the one that makes treatment recommendations based on the true parameter values as opposed to their estimates. We also look at bias and mean squared error of the individual parameter-estimates as secondary metrics. Finally, we analyze the STAR*D data using the proposed method.

Coronavirus disease 2019 (COVID-19), a highly infectious disease, was first detected in Wuhan, China, in December 2019. The disease has spread to 212 countries and territories around the world and infected (confirmed) more than three million people. In India, the disease was first detected on 30 January 2020 in Kerala in a student who returned from Wuhan. The total (cumulative) number of confirmed infected people is more than 37000 till now across India (3 May 2020). Most of the research and newspaper articles focus on the number of infected people in the entire country. However, given the size and diversity of India, it may be a good idea to look at the spread of the disease in each state separately, along with the entire country. For example, currently, Maharashtra has more than 10000 confirmed cumulative infected cases, whereas West Bengal has less than 800 confirmed infected cases (1 May 2020). The approaches to address the pandemic in the two states must be different due to limited resources. In this article, we will focus the infected people in each state (restricting to only those states with enough data for prediction) and build three growth models to predict infected people for that state in the next 30 days. The impact of preventive measures on daily infected-rate is discussed for each state.

Xanthomonas oryzae pv. oryzae uses several type III secretion system (T3SS) secreted effectors, namely XopN, XopQ, XopX and XopZ, to suppress rice immune responses that are induced following treatment with cell wall degrading enzymes. Here we show that a T3SS secreted effector XopX interacts with two of the eight rice 14-3-3 proteins. Mutants of XopX that are defective in 14-3-3 binding are also defective in suppression of immune responses, suggesting that interaction with 14-3-3 proteins is required for suppression of host innate immunity. However, Agrobacterium-mediated delivery of both XopQ and XopX into rice cells results in induction of rice immune responses. These immune responses are not observed when either protein is individually delivered into rice cells. XopQ-XopX-induced rice immune responses are not observed with a XopX mutant that is defective in 14-3-3 binding. Yeast two-hybrid, bimolecular fluorescence complementation and co-immunoprecipitation assays indicate that XopQ and XopX interact with each other. A screen for Xanthomonas effectors that can suppress XopQ-XopX-induced rice immune responses led to the identification of five effectors, namely XopU, XopV, XopP, XopG and AvrBs2, that could individually suppress these immune responses. These results suggest a complex interplay of Xanthomonas T3SS effectors in suppression of both pathogen-triggered immunity and effector-triggered immunity to promote virulence on rice.