Background: The novel COVID-19 disease has spread worldwide, resulting in a new pandemic. The Chinese government implemented strong intervention measures in the early stage of the epidemic, including strict travel bans and social distancing policies. Prioritizing the analysis of different contributing factors to outbreak outcomes is important for the precise prevention and control of infectious diseases. We proposed a novel framework for resolving this issue and applied it to data from China. Objective: This study aimed to systematically identify national-level and city-level contributing factors to the control of COVID-19 in China. Methods: Daily COVID-19 case data and related multidimensional data, including travel-related, medical, socioeconomic, environmental, and influenza-like illness factors, from 343 cities in China were collected. A correlation analysis and interpretable machine learning algorithm were used to evaluate the quantitative contribution of factors to new cases and COVID-19 growth rates during the epidemic period (ie, January 17 to February 29, 2020). Results: Many factors correlated with the spread of COVID-19 in China. Travel-related population movement was the main contributing factor for new cases and COVID-19 growth rates in China, and its contributions were as high as 77% and 41%, respectively. There was a clear lag effect for travel-related factors (previous vs current week: new cases, 45% vs 32%; COVID-19 growth rates, 21% vs 20%). Travel from non-Wuhan regions was the single factor with the most significant impact on COVID-19 growth rates (contribution: new cases, 12%; COVID-19 growth rate, 26%), and its contribution could not be ignored. City flow, a measure of outbreak control strength, contributed 16% and 7% to new cases and COVID-19 growth rates, respectively. Socioeconomic factors also played important roles in COVID-19 growth rates in China (contribution, 28%). Other factors, including medical, environmental, and influenza-like illness factors, also contributed to new cases and COVID-19 growth rates in China. Based on our analysis of individual cities, compared to Beijing, population flow from Wuhan and internal flow within Wenzhou were driving factors for increasing the number of new cases in Wenzhou. For Chongqing, the main contributing factor for new cases was population flow from Hubei, beyond Wuhan. The high COVID-19 growth rates in Wenzhou were driven by population-related factors. Conclusions: Many factors contributed to the COVID-19 outbreak outcomes in China. The differential effects of various factors, including specific city-level factors, emphasize the importance of precise, targeted strategies for controlling the COVID-19 outbreak and future infectious disease outbreaks.

Antibodies specifically bind to antigens and are an essential part of the immune system. Hence, antibodies are powerful tools in research and diagnostics. High-throughput sequencing technologies have promoted comprehensive profiling of the immune repertoire, which has resulted in large amounts of antibody sequences that remain to be further analyzed. In this study, antibodies were downloaded from IMGT/LIGM-DB and Sequence Read Archive databases. Contributing features from antibody heavy chains were formulated as numerical inputs and fed into an ensemble machine learning classifier to classify the antigen specificity of six classes of antibodies, namely anti-HIV-1, anti-influenza virus, anti-pneumococcal polysaccharide, anti-citrullinated protein, anti-tetanus toxoid and anti-hepatitis B virus. The classifier was validated using cross-validation and a testing dataset. The ensemble classifier achieved a macro-average area under the receiver operating characteristic curve (AUC) of 0.9246 from the 10-fold cross-validation, and 0.9264 for the testing dataset. Among the contributing features, the contribution of the complementarity-determining regions was 53.1% and that of framework regions was 46.9%, and the amino acid mutation rates occupied the first and second ranks among the top five contributing features. The classifier and insights provided in this study could promote the mechanistic study, isolation and utilization of potential therapeutic antibodies.

Background: The COVID‐19 pandemic disrupted global influenza transmission. We aimed to elucidate how meteorological and air pollution drivers influenced seasonal influenza A subtypes and B lineage in Southern China pre‐, during‐, and postpandemic. Methods: We analyzed weekly influenza surveillance data from Southern China (2011–2024) and corresponding meteorological data. Using an interpretable machine learning framework combining XG‐Boost and SHapley Additive exPlanation (SHAP), we quantified the dynamic contributions of environmental factors to influenza subtype positivity rates. GAM models were employed to identify climate thresholds for influenza transmission. Findings: The pandemic reshaped influenza transmission and environmental dependencies in Southern China. Post‐pandemic circulation showed marked subtype‑specific shifts: A/H3N2 winter positivity nearly tripled (12.670% vs. 4.861% pre‐pandemic, p < 0.001), A/H1N1 increased over four‑fold (4.325% vs. 0.935% pre‐pandemic, p = 0.064), while B/Victoria circulation declined significantly (0.594% vs. 1.623% pre‐pandemic, p < 0.001). Environmental driver hierarchies underwent notable temporal reorganization. For A/H3N2, PM ₁₀ influence surged from 9.18% to 30.37% during the pandemic before dropping to 3.95% post‐pandemic, while visibility emerged as the dominant driver (58.05% vs. 37.26% pre‐pandemic). A/H1N1 showed peak humidity sensitivity during the pandemic (18.59%) that later diminished (3.78%), with transmission promotion occurring only below a threshold of ≤11.44 g/m³ absolute humidity. B/Victoria maintained consistent sea‑level pressure sensitivity, peaking post‐pandemic (36.36%), with transmission optima at 8.58 g/m³. Seasonal modeling revealed subtype and phase‑specific environmental thresholds that shifted between “promotion” and “inhibition” zones for humidity and visibility, indicating key alterations in climate‑influenza relationships across the pandemic transition. Interpretation: These findings show that pandemic‑era disruptions recalibrated the environmental architecture of influenza transmission. New environmental thresholds and driver hierarchies reveal shifts in viral‑climate relationships. The identification of distinct “promotion‑inhibition” zones for each subtype establishes a new paradigm for climate‑pathogen interactions. These insights require immediate integration into surveillance frameworks and predictive models.

Background: Hand, foot, and mouth disease (HFMD) is a global health concern requiring a risk assessment framework based on systematic factors analysis for prevention and control. Objective: This study aims to construct a comprehensive HFMD risk assessment framework by integrating multisource data, including historical incidence information, environmental parameters, and web-based search behavior data, to improve predictive performance. Methods: We integrated multisource data (HFMD cases, meteorology, air pollution, Baidu Index, and public health measures) from Bao’an District of Shenzhen city in Southern China (2014‐2023). Correlation analysis was used to assess the associations between HFMD incidence and systematic factors. The impacts of environmental factors were analyzed using the Distributed Lag Nonlinear Model. Seasonal Autoregressive Integrated Moving Average model and advanced machine learning methods were used to predict HFMD 1-4 weeks ahead. Risk levels for the 1- to 4-week-ahead forecasts were determined by comparing the predicted weekly incidence against predefined thresholds. Results: From 2014 to 2023, Bao’an District reported a total of 118,826 cases of HFMD. Environmental and search behavior factors (excluding sulfur dioxide) were significantly associated with HFMD incidence in nonlinear patterns. For 1-week-ahead prediction, Seasonal Autoregressive Integrated Moving Average using case data alone performed best ( R ²=0.95, r =0.98, mean absolute error=53.34, and root-mean-square error=99.31). For 2- to 4-week-ahead forecasting, machine learning models incorporating web-based and environmental data showed superior performance ( R ²=0.83, 0.75, and 0.64; r =0.92, 0.87, and 0.80; mean absolute error=87.84, 112.41, and 132.47; and root-mean-square error=185.08, 229.13, and 276.81). The predicted HFMD risk levels matched the observed levels with accuracies of 96%, 87%, 88%, and 83%, respectively. Conclusions: The epidemic dynamics of HFMD are influenced by multiple factors in a nonlinear manner. Integrating multisource data, particularly web-based search behavior, significantly enhances the accuracy of short- and midterm forecasts and risk assessment. This approach offers practical insights for developing digital surveillance and early warning systems in public health.

Background: The COVID-19 pandemic has transitioned into an endemic phase with heterogeneous resurgences. Despite widespread vaccination and public health measures, the interplay of viral evolution, population immunity, and environmental factors drives diverse global patterns of COVID-19 burden. However, how these systematic factors dynamically shape disease transmission and severity across populations remains incompletely understood. Objective: This study aims to determine the relative contributions and temporal dynamics of viral variants, population immunity (natural infection and vaccination), environmental conditions, and public health measures in determining COVID-19 disease burden. Methods: This retrospective longitudinal time-series study used a big data-driven interpretable machine learning approach to analyze global multifaceted data across 38 countries from pandemic onset through December 31, 2022. Daily time-series data encompassing viral variants, natural infection, vaccination coverage, environmental conditions, policy interventions, health care infrastructure, and migration trends were integrated. The gradient-boosted trees (XGBoost [extreme gradient boosting]) model, coupled with Shapley Additive Explanations interpretation, quantifies the complex interdependencies and their spatiotemporal effects on 4 COVID-19 burden metrics—effective reproduction number (Rt), hospitalizations, intensive care unit (ICU) admissions, and deaths. Results: Variant-related factors dominance drives transmission/Rt (24.02%, 95% CI 10.10-66.88 contribution) but progressively attenuates across severe outcomes (4.24%, 95% CI 1.59-10.89 for ICU; 5.52%, 95% CI 1.94-15.39 for deaths). Omicron 21K and Delta 21J demonstrate exceeding baseline transmissibility by 12.2% and 3.4% respectively. Conversely, immunity-related factors show inverse patterns: natural infection contributions escalate with severity (12.82% for Rt, 14.91% for hospitalization, 21.96% for ICU [95% CI 7.36-47.55], rising to 36.00% [95% CI 10.25-78.56] for deaths). COVID-19 vaccination maintains substantial influence on severe outcomes (18.04% [95% CI 6.39-42.49] for ICU; 20.31% [95% CI 6.53-58.31] for deaths), with protective critical population thresholds: 29.9% (95% CI 29.8-29.9) coverage for transmission reduction and 72.3% (95% CI 72.2-72.8) for ICU prevention. Routine immunizations exhibit cross-protective effects, particularly the yellow fever vaccine at doses exceeding 600,000 for Rt reduction and >100,000 for ICU protection. Temperature demonstrates threshold effects: 14.95°C (95% CI 14.86-15.43) for hospitalizations and 11.89°C (95% CI 11.81-11.97) for ICU admissions. Health care infrastructure contributed 23.98% (95% CI 7.03-73.13) to hospitalization outcomes. Conclusions: The large-scale epidemiological data mining reveals previously unrecognized patterns through three innovations: (1) quantifying variant evolutionary fitness with transmission thresholds, (2) identifying dual vaccination coverage thresholds for transmission versus severe disease prevention, and (3) discovering dose-specific cross-protection from routine immunizations. Unlike black-box predictions, this interpretable framework integrates multidomain surveillance data to reveal how variants, immunity, and environment jointly shape disease burden with temporal resolution. Real-world applications include tiered vaccination strategies targeting specific coverage goals, variant surveillance prioritizing lineages with demonstrated fitness in contemporary immunity contexts, and expanding routine immunization programs as pandemic preparedness measures. This framework provides quantifiable benchmarks for adaptive pandemic response across immunization strategies, variant surveillance, and health care capacity planning.

In order to predict the types of membrane protein accurately, this paper firstly proposed a fusion feature representation, which contains a more comprehensive information of the original protein sequence by fusing two single feature expressions, pseudo amino acid composition (PseAAC) and dipeptide composition (DipC). Then, we proposed an improved support vector machine (SVM) method by introducing the idea of kernel function to evaluate prediction performance of the new fusion representation. In addition, we have deeply studied the influence of three different kernel functions as well as their kernel parameters on the prediction of membrane protein types.-Through experimental verification, it shows that the proposed integration representation with our improved SVM has a good performance in the prediction of membrane protein types. The final overall prediction accuracy can reach up to 89.64% under the Jackknife test method.