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BACKGROUND: Severe Plasmodium falciparum malaria is a major cause of death in children. The contribution of the parasite burden to the pathogenesis of severe malaria has been controversial. METHODS: We documented P. falciparum infection and disease in Tanzanian children followed from birth for an average of 2 years and for as long as 4 years. RESULTS: Of the 882 children in our study, 102 had severe malaria, but only 3 had more than two episodes. More than half of first episodes of severe malaria occurred after a second infection. Although parasite levels were higher on average when children had severe rather than mild disease, most children (67 of 102) had high-density infection (>2500 parasites per 200 white cells) with only mild symptoms before severe malaria, after severe malaria, or both. The incidence of severe malaria decreased considerably after infancy, whereas the incidence of high-density infection was similar among all age groups. Infections before and after episodes of severe malaria were associated with similar parasite densities. Nonuse of bed nets, placental malaria at the time of a womans second or subsequent delivery, high-transmission season, and absence of the sickle cell trait increased severe-malaria risk and parasite density during infections. CONCLUSIONS: Resistance to severe malaria was not acquired after one or two mild infections. Although the parasite burden was higher on average during episodes of severe malaria, a high parasite burden was often insufficient to cause severe malaria even in children who later were susceptible. The diverging rates of severe disease and high-density infection after infancy, as well as the similar parasite burdens before and after severe malaria, indicate that naturally acquired resistance to severe malaria is not explained by improved control of parasite density. (Funded by the National Institute of Allergy and Infectious Diseases and others.).

We assessed human immunodeficiency virus (HIV) load in plasma and semen during primary HIV infection using serial samples of semen and plasma during the first 24 weeks after diagnosis in untreated participants and those who started antiretroviral therapy (ART) immediately at diagnosis. In the absence of treatment, semen viral load was >1000 copies/mL in almost all specimens (83%) collected 2–10 weeks after the estimated date of HIV acquisition and remained >1000 copies/mL in 35% of untreated participants at the last observed time point. Thus, in the absence of ART, semen viral load remained at a level consistent with transmissibility throughout primary infection. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficient statistical method (denoted as 'Adaptive Resources Allocation CUSUM') to robustly and efficiently detect the hotspot with limited sampling resources. Our main idea is to combine the multi-arm bandit (MAB) and change-point detection methods to balance the exploration and exploitation of resource allocation for hotspot detection. Further, a Bayesian weighted update is used to update the posterior distribution of the infection rate. Then, the upper confidence bound (UCB) is used for resource allocation and planning. Finally, CUSUM monitoring statistics to detect the change point as well as the change location. For performance evaluation, we compare the performance of the proposed method with several benchmark methods in the literature and showed the proposed algorithm is able to achieve a lower detection delay and higher detection precision. Finally, this method is applied to hotspot detection in a real case study of county-level daily positive COVID-19 cases in Washington State WA) and demonstrates the effectiveness with very limited distributed samples. [ABSTRACT FROM AUTHOR]

Background Transmission of human immunodeficiency virus type 1 (HIV-1) drug resistance mutations, particularly that of minority drug-resistant variants, remains poorly understood. Population-based studies suggest that drug-resistant HIV-1 is less transmissible than drug-susceptible viruses. We compared HIV-1 drug-resistant genotypes among partner-pairs in order to assess the likelihood of transmission of drug resistance mutations and investigate the role of minority variants in HIV transmission. Methods and findings From 1992-2010, 340 persons with primary HIV-1 infection and their partners were enrolled into observational research studies at the University of Washington Primary Infection Clinic (UWPIC). Out of 50 partner-pairs enrolled, 36 (72%) transmission relationships were confirmed by phylogenetic distance analysis of HIV-1 envelope (env) sequences, and 31 partner-pairs enrolled after 1995 met criteria for this study. Drug resistance mutations in the region of the HIV-1 polymerase gene (pol) that encodes protease and reverse transcriptase were assessed by 454-pyrosequencing. In 25 partner-pairs where the transmission direction could be determined, 12 (48%) transmitters had 1-4 drug resistance mutations (23 total) detected in their HIV-1 populations at a median frequency of 6.0% (IQR 1.5%-98.7%, range 1.0%-99.6%). Of 10 major mutations detected in five transmitters at a frequency >95%, 100% (95% CI 69.2%-100%) were detected in recipients. All of these transmitters were antiretroviral (ARV)-naïve at the time of specimen collection. Fourteen mutations (eight major mutations and six accessory mutations) were detected in nine transmitters at low frequencies (1.0%-11.8%); four of these transmitters had previously received ARV therapy. Two (14% [95% CI 1.8%-42.8%]) G73S accessory mutations were detected in both transmitter and recipient. This number is not significantly different from the number expected based on the observed frequencies of drug-resistant viruses in transmitting partners. Limitations of this study include the small sample size and uncertainties in determining the timing of virus transmission and mutation history. Conclusions Drug-resistant majority variants appeared to be commonly transmitted by ARV-naïve participants in our analysis and may contribute significantly to transmitted drug resistance on a population level. When present at low frequency, no major mutation was observed to be shared between partner-pairs; identification of accessory mutations shared within a pair could be due to transmission, laboratory artifact, or apolipoprotein B mRNA-editing enzyme, catalytic polypeptides (APOBECs), and warrants further study., Author(s): Joanne D. Stekler 1,*, Ross Milne 1,2, Rachel Payant 2, Ingrid Beck 2, Joshua Herbeck 1, Brandon Maust 1, Wenjie Deng 1, Kenneth Tapia 1, Sarah Holte 1,3, Janine [...]

In many real-world applications of monitoring multivariate spatio-temporal data that are non-stationary over time, one is often interested in detecting hot-spots with spatial sparsity and temporal consistency, instead of detecting system-wise changes as in traditional statistical process control (SPC) literature. In this paper, we propose an efficient method to detect hot-spots through tensor decomposition, and our method has three steps. First, we fit the observed data into a Smooth Sparse Decomposition Tensor (SSD-Tensor) model that serves as a dimension reduction and de-noising technique: it is an additive model decomposing the original data into: smooth but non-stationary global mean, sparse local anomalies, and random noises. Next, we estimate model parameters by the penalized framework that includes Least Absolute Shrinkage and Selection Operator (LASSO) and fused LASSO penalty. An efficient recursive optimization algorithm is developed based on Fast Iterative Shrinkage Thresholding Algorithm (FISTA). Finally, we apply a Cumulative Sum (CUSUM) Control Chart to monitor model residuals after removing global means, which helps to detect when and where hot-spots occur. To demonstrate the usefulness of our proposed SSD-Tensor method, we compare it with several other methods including scan statistics, LASSO-based, PCA-based, T2-based control chart in extensive numerical simulation studies and a real crime rate dataset. [ABSTRACT FROM AUTHOR]