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Dengue hemorrhagic fever (DHF), a severe manifestation of dengue viral infection that can cause severe bleeding, organ impairment, and even death, affects between 15,000 and 105,000 people each year in Thailand. While all Thai provinces experience at least one DHF case most years, the distribution of cases shifts regionally from year to year. Accurately forecasting where DHF outbreaks occur before the dengue season could help public health officials prioritize public health activities. We develop statistical models that use biologically plausible covariates, observed by April each year, to forecast the cumulative DHF incidence for the remainder of the year. We perform cross-validation during the training phase (2000–2009) to select the covariates for these models. A parsimonious model based on preseason incidence outperforms the 10-y median for 65% of province-level annual forecasts, reduces the mean absolute error by 19%, and successfully forecasts outbreaks (area under the receiver operating characteristic curve = 0.84) over the testing period (2010–2014). We find that functions of past incidence contribute most strongly to model performance, whereas the importance of environmental covariates varies regionally. This work illustrates that accurate forecasts of dengue risk are possible in a policy-relevant timeframe.

Modelling the effects of environmental change on populations is a key challenge for ecologists, particularly as the pace of change increases. Currently, modelling efforts are limited by difficulties in establishing robust relationships between environmental drivers and population responses. We developed an integrated capture–recapture state‐space model to estimate the effects of two key environmental drivers (stream flow and temperature) on demographic rates (body growth, movement and survival) using a long‐term (11 years), high‐resolution (individually tagged, sampled seasonally) data set of brook trout (Salvelinus fontinalis) from four sites in a stream network. Our integrated model provides an effective context within which to estimate environmental driver effects because it takes full advantage of data by estimating (latent) state values for missing observations, because it propagates uncertainty among model components and because it accounts for the major demographic rates and interactions that contribute to annual survival. We found that stream flow and temperature had strong effects on brook trout demography. Some effects, such as reduction in survival associated with low stream flow and high temperature during the summer season, were consistent across sites and age classes, suggesting that they may serve as robust indicators of vulnerability to environmental change. Other survival effects varied across ages, sites and seasons, indicating that flow and temperature may not be the primary drivers of survival in those cases. Flow and temperature also affected body growth rates; these responses were consistent across sites but differed dramatically between age classes and seasons. Finally, we found that tributary and mainstem sites responded differently to variation in flow and temperature. Annual survival (combination of survival and body growth across seasons) was insensitive to body growth and was most sensitive to flow (positive) and temperature (negative) in the summer and fall. These observations, combined with our ability to estimate the occurrence, magnitude and direction of fish movement between these habitat types, indicated that heterogeneity in response may provide a mechanism providing potential resilience to environmental change. Given that the challenges we faced in our study are likely to be common to many intensive data sets, the integrated modelling approach could be generally applicable and useful.

[This corrects the article DOI: 10.1371/journal.pntd.0004761.].[This corrects the article DOI: 10.1371/journal.pntd.0004761.].

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making.

Regression analyses are central to characterization of the form and strength of natural selection in nature. Two common analyses that are currently used to characterize selection are (1) least squares—based approximation of the individual relative fitness surface for the purpose of obtaining quantitatively useful selection gradients, and (2) spline-based estimation of (absolute) fitness functions to obtain flexible inference of the shape of functions by which fitness and phenotype are related. These two sets of methodologies are often implemented in parallel to provide complementary inferences of the form of natural selection. We unify these two analyses, providing a method whereby selection gradients can be obtained for a given observed distribution of phenotype and characterization of a function relating phenotype to fitness. The method allows quantitatively useful selection gradients to be obtained from analyses of selection that adequately model nonnormal distributions of fitness, and provides unification of the two previously separate regression-based fitness analyses. We demonstrate the method by calculating directional and quadratic selection gradients associated with a smooth regression-based generalized additive model of the relationship between neonatal survival and the phenotypic traits of gestation length and birth mass in humans.

Statistical prediction models inform decision-making processes in many real-world settings. Prior to using predictions in practice, one must rigorously test and validate candidate models to ensure that the proposed predictions have sufficient accuracy to be used in practice. In this article, we present a framework for evaluating time series predictions, which emphasizes computational simplicity and an intuitive interpretation using the relative mean absolute error metric. For a single time series, this metric enables comparisons of candidate model predictions against naïve reference models, a method that can provide useful and standardized performance benchmarks. Additionally, in applications with multiple time series, this framework facilitates comparisons of one or more models' predictive performance across different sets of data. We illustrate the use of this metric with a case study comparing predictions of dengue hemorrhagic fever incidence in two provinces of Thailand. This example demonstrates the utility and interpretability of the relative mean absolute error metric in practice, and underscores the practical advantages of using relative performance metrics when evaluating predictions.

BackgroundRECIST1 is commonly used to characterize intermediate outcomes for clinical trials in the context of solid tumors, and it is largely based on a standardized measure of tumor size known as the sum of longest diameters (SLD). In recent years, the FDA has granted accelerated approvals for several new compounds based on improvements in RECIST-based surrogate outcomes like overall response rate and progression-free survival.2 However, there are concerns regarding the robustness of these surrogate endpoints relative to overall survival (OS),3 4 and it is not known whether their prognostic value is similar across TKI, chemotherapy, and immunotherapy regimens.MethodsWe have developed a Bayesian meta-analytic joint model for longitudinal SLD and OS in order to predict Phase III outcomes from early Phase II data. We validated this model in extensive simulation studies. The model utilizes a generalized Stein-Fojo equation5 to characterize SLD over time in terms of 3 parameters: f (proportion of tumor that is treatment-susceptible), ks (the decay rate among susceptible cells), and kg (the growth rate among resistant cells). Two quantities [tumor shrinkage (f * ks) and tumor regrowth ((1-f) * kg)] are then associated with survival in the context of a proportional-hazards survival model. We estimated this model using Stan6 on a dataset of >6,000 subjects in 15 randomized clinical trials in advanced non-small cell lung cancer.ResultsBoth tumor shrinkage and tumor regrowth were found to be associated with OS (HR for tumor shrinkage: median 0.51, 90% CrI 0.42 - 0.61; HR for tumor regrowth: median 1.24, 90% CrI 1.18 - 1.32). There is a stronger association between tumor shrinkage and OS among patients randomized to a PD-1/PD-L1 inhibitor, either as a monotherapy or in combination with a CTLA-4 inhibitor, than among patients in other trial arms (figure 1). By contrast, there were negligible differences across treatment classes in the association between tumor regrowth and OS.Abstract 276 Figure 1Hazard associated with SLD submodel parameters varies according to the class of treatment in a joint model for SLD and overall survival with varying association by assigned treatment regimen. The points represent posterior median values per treatment, with lines representing 90% posterior credible intervals (CrI). Two treatment classes demonstrated posterior probability greater than 90% of a non-zero treatment-specific effect for the response term: the combination PD-1/PD-L1 inhibitor + CTLA-4 inhibitor [interaction HR = 0.64 (90% CrI 0.39 - 1.00; posterior probability of HR<1: 95.2%)] and the PD-1/PD-L1 inhibitor alone [interaction HR = 0.62 (90% CrI 0.42 - 0.89; posterior probability of HR<1: 99.2%)].[Figure omitted. See PDF]ConclusionsOur results suggest that not all reductions in tumor size are equal. A patient with a certain degree of tumor shrinkage on the PD-1/PD-L1 inhibitor will have lower mortality risk than a patient with a similar degree of shrinkage on the other regimens evaluated. More research is needed to determine whether the result is unique to this particular PD-1/PD-L1 inhibitor, to determine what mechanisms of action mediate these treatment-specific effects, and to develop improved surrogate measures of treatment efficacy.ReferencesEisenhauer EA, Therasse P, Bogaerts J, et al: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009;45:228–247.Of Health USD, Services H, Others: Clinical trial endpoints for the approval of non-small cell lung cancer drugs and biologics: guidance for Industry, 2015Mandrekar SJ, An M-W, Meyers J, et al: Evaluation of Alternate Categorical Tumor Metrics and Cut Points for Response Categorization Using the RECIST 1.1 Data Warehouse. J Clin Orthod 32:841–850, 2014Blumenthal GM, Karuri SW, Zhang H, et al: Overall response rate, progression-free survival, and overall survival with targeted and standard therapies in advanced non-small-cell lung cancer: US Food and Drug Administration trial-level and patient-level analyses. J Clin Oncol 33:1008–1014, 2015Stein WD, Figg WD, Dahut W, et al: Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: a novel strategy for evaluation of clinical trial data. Oncologist 13:1046–1054, 2008Carpenter B, Gelman A, Hoffman MD, et al: Stan: A probabilistic programming language [Internet]. jitc-2020-SITC2020.0277.pdf

Abstract Disparities in coronavirus disease 2019 mortality are driven by inequalities in group-specific incidence rates (IRs), case fatality rates (CFRs), and their interaction. For emerging infections, such as severe acute respiratory syndrome coronavirus 2, group-specific IRs and CFRs change on different time scales, and inequities in these measures may reflect different social and medical mechanisms. To be useful tools for public health surveillance and policy, analyses of changing mortality rate disparities must independently address changes in IRs and CFRs. However, this is rarely done. In this analysis, we examine the separate contributions of disparities in the timing of infection—reflecting differential infection risk factors such as residential segregation, housing, and participation in essential work—and declining CFRs over time on mortality disparities by race/ethnicity in the US state of Michigan. We used detailed case data to decompose race/ethnicity-specific mortality rates into their age-specific IR and CFR components during each of 3 periods from March to December 2020. We used these estimates in a counterfactual simulation model to estimate that that 35% (95% credible interval, 30%–40%) of deaths in black Michigan residents could have been prevented if these residents were infected along the timeline experienced by white residents, resulting in a 67% (61%–72%) reduction in the mortality rate gap between black and white Michigan residents during 2020. These results clearly illustrate why differential power to “wait out” infection during an infectious disease emergency—a function of structural racism—is a key, underappreciated, driver of inequality in disease and death from emerging infections. Data from Michigan demonstrate how structural racism drove inequality in COVID-19 mortality via its impact on the time ordering of infection, due to differential risks of exposure across racial and ethnic groups when case fatality rates were high.