Kishor Kumar Paul

Background: Influenza is a leading cause of pediatric hospitalizations, with clinical presentations and outcomes varying between types A and B. This study compared the clinical characteristics and complications of children hospitalized with influenza A and B at the Sydney Children's Hospitals Network (SCHN) in the post‐COVID era. Methods: We conducted a retrospective cohort study of children < 18 years admitted to SCHN with influenza during 2022–2023. Data on patient demographics, clinical presentations, complications, and management were extracted from electronic medical records. Group comparisons used chi‐square or Fisher's exact tests, and modified Poisson regression estimated adjusted risk ratios (RRs) with 95% confidence intervals (CIs) for key clinical outcomes. Results: Among 704 influenza hospitalizations, 480 (68.2%) were due to influenza A and 224 (31.8%) to influenza B. Influenza B hospitalizations were more frequent in older children aged 5–< 18 years (67.4% vs. 57.7%) and associated with myalgia and pharyngitis, whereas influenza A hospitalizations were more common in children < 5 years (42.3% vs. 32.6%) and linked to cough, wheeze, and seizures. Although less common, influenza B hospitalizations exhibited greater clinical severity, with higher risks of noninvasive respiratory support (RR = 2.94; 95% CI 1.49–5.81) and intensive care unit (ICU) admission and/or mechanical ventilation (RR = 3.23; 95% CI 1.43–7.32). Conclusions: Influenza B, though less prevalent, was associated with greater severity, particularly in older children. These findings underscore the importance of continued protection against both influenza types and support consideration of expanding vaccine access to children aged 5 years and older.

During epidemics, emergency department (ED) syndromic surveillance of patient arrivals provides timely but nonvirus-specific assessment of epidemic intensity. Surveillance of severe infection outcomes (intensive care admission or death) is less timely because outcomes can take weeks to occur. Time series models can be used to estimate the frequency of severe infection outcomes due to viruses. We developed and evaluated daily time series modeling applied to linked ED, infection, and outcomes data from Australia to better predict population and health system burden during acute respiratory virus epidemics. In retrospective daily surveillance emulation, generalized additive models nowcasted (short-term forecast) the frequency of ED arrivals attributable to each of influenza and COVID-19 that will have a severe infection outcome within 28 days. Daily nowcasts spanned days -29 to -4 from each date for which surveillance was emulated. To validate the method, nowcasts were compared with subsequently observed severe infection outcome frequencies for December 2021 through February 2023. During this period, the mean daily day-4 nowcast error was 2.7 (34.2%), compared with 3.5 (43.8%) if outcomes known at day-1 were used. With increasing real-world data availability, this method could improve rapid, automated epidemic assessment for timely public health action.

Multiplex immunoassays can facilitate the parallel measurement of antibody responses against multiple antigenically related pathogens, generating a wealth of high-dimensional data that depict complex antibody-antigen relationships. In this study, we developed a generalizable analytical framework to maximize inferences from multipathogen serological studies. We fit the model to measurements of immunoglobulin antibody binding against 10 arboviral pathogens from a cross-sectional study in northwest Bangladesh with 1453 participants. We used our framework to jointly infer the prevalence of each pathogen by location and age as well as between-pathogen antibody cross-reactivity. Reconstructing immunological profiles, we found evidence of endemic transmission of Japanese encephalitis virus and recent outbreaks of dengue and chikungunya viruses in this district. Our estimates of antibody cross-reactivity were highly correlated with phylogenetic distances inferred from genetic data [correlation coefficient ( r ) = 0.94], demonstrating how antigenic landscapes can be inferred from population-level serological studies. Furthermore, we showed how our framework could be used to identify the presence of antigenically related pathogens that were not directly tested for, representing a potential opportunity for the detection of emerging pathogens. The presented analytical framework offers a tool that can be applied to a growing number of multipathogen studies and will help support the integration of serological testing into disease surveillance platforms.

Background: Hepatitis E virus (HEV) is endemic in many resource-poor countries. Despite an available vaccine, data on HEV-associated mortality are scarce, hindering informed decisions. This study aims to estimate the population-based rate of HEV-specific mortality in Bangladesh. Methods: During December 2014 to September 2017, we conducted surveillance in 6 tertiary hospitals in Bangladesh. Patients aged ≥14 years with acute jaundice were recruited, tested for IgM anti-HEV, and followed up postdischarge. A mortality survey in the hospital catchment areas identified deaths associated with acute jaundice, including maternal deaths, stillbirths, and neonatal deaths delivered by a mother with acute jaundice during pregnancy, as confirmed by 2 independent physicians reviewing verbal autopsy data. Results: Out of 1925 patients with acute jaundice enrolled in the surveillance hospitals, 302 died, with 28 (9%) testing positive for IgM anti-HEV. In the hospital catchment areas, the team identified 587 jaundice-associated deaths, including 25 maternal deaths. Combining hospital-based surveillance and mortality survey data, the study estimated 986 (95% CI, 599–1338) HEV-associated deaths annually among individuals aged ≥14 years in Bangladesh, including 163 (95% CI, 57–395) maternal deaths. Additionally, 279 (95% CI, 101–664) stillbirths and 780 (95% CI, 365–1297) neonatal deaths were attributed to HEV infection annually. Conclusions: Prior Global Burden of Disease studies presented wildly varying modeling estimates of HEV-associated annual deaths, ranging from 50 000 in 2013 to 1932 in 2019. This study is the first to directly measure population-based estimates of mortality in Bangladesh, which can be used to determine the cost-effectiveness of hepatitis E vaccination and other interventions.

Dengue is a major public health concern in tropical and sub-tropical areas of the world. Dengue virus is transmitted to humans primarily by female Aedes aegypti mosquitoes and its epidemiology is heavily influenced by the climate. We developed a mechanistic compartmental model to characterize how climate and the evolution of population-level immunity influence changes in the epidemiology of dengue. The model explicitly incorporates aquatic and adult stages of the mosquito vector and cross-immunity (to another serotype) to better represent the transmission of dengue compared to previous models. It incorporates parameters related to mosquito development, behaviour, and mortality that are dependent on temperature, rainfall, and humidity. We calibrated the model to available dengue seroprevalence estimates for Dhaka, Bangladesh as it is a well-observed densely populated endemic area for dengue and will be strongly impacted by climate change. We then simulated dengue epidemiology for the period of 1995-2014 and compared the simulation output with available epidemiologic data. The model produced repeating annual outbreaks of dengue with strong seasonality, as observed in the dengue case reports for Dhaka. The median number of annual dengue infections produced by the model was 1.6 million cases (IQR: 0.6-2.3 million) and the median of the annual daily peak size of dengue infections was 24,121 (IQR: 7,807-39,308) across the simulations. These figures while exceeding reported cases in Dhaka, which do not include asymptomatic infections or cases not seeking healthcare, align with previous estimates. The modelled seroprevalence of 62% (IQR: 58– 75%) aligned with survey results from Dhaka. In most model simulations, dengue infections occurred during August to December with the median peak occurring in October. The model outputs demonstrated a high degree of conformity to the reported data in terms of seasonality, and seroprevalence, indicating that the model provides a good representation of overall dengue epidemiology in Dhaka. By accounting for climate variables, host immunity, and their interplay, our model is well-suited for evaluating the long-term impact of climate change on dengue transmission in Dhaka and other settings where climate and epidemiological data are available.

Japanese encephalitis virus (JEV) is a major threat to human health. Bangladesh is considering introducing a JEV vaccine; however, the investment case is hampered by a limited understanding of key aspects of JEV ecology. We conducted a seroprevalence study in a high-incidence region using an assay that limits cross-reactivity with dengue virus. We also trapped mosquitoes and collected information about potential host species. We used mathematical models to recover risk factors for infection and underlying probabilities of severe disease and death. We observed 19.0% [95% confidence interval (CI):17.1 to 21.1] of JEV antibodies. On average, 0.7% (95% CI: 0.2 to 2.0) of the susceptible population gets infected yearly, with pig proximity being the main human infection risk factor. Our traps captured 10 different mosquito species that have been linked with JEV transmission. We estimated that 1 in 1000 infections results in severe disease, 1 in 10,000 results in death, and 76% of severe cases are missed by surveillance.

In 2023, Bangladesh experienced its largest and deadliest outbreak of Dengue virus (DENV), reporting the highest-ever recorded annual cases and deaths. We aimed to characterize the geographical transmission of the DENV in Bangladesh. From 1 Jan – 31 Dec 2023, we extracted and analyzed daily data on dengue cases and deaths from the national Management Information System (MIS). We performed a generalized linear mixed model to identify the associations between division-wise daily dengue counts and various geographical and meteorological covariates. The number of Dengue cases reported in 2023 was 1.3 times higher than the total number recorded in the past 23 years (321,179 vs. 244,246), with twice as many deaths than the total fatalities recorded in the past 23 years (1705 vs. 849). Of the 1705 deaths in 2023, 67.4% (n=1015) expired within one day after hospital admission. The divisions southern to Dhaka had a higher dengue incidence/1000 population (2.30 vs. 0.50, p<0,0.01), and higher mean annual temperatures (27.46 vs. 26.54 °C) than the northern divisions. The average daily temperature (IRR: 1.13, 95% CI: 1.11-1.14), urban and rural population ratio of the divisions (IRR: 1.04, 95% CI: 1.03-1.04), showed a positive, and rainfall (IRR: 0.99, 95% CI: 0.98-0.99) showed a negative association with dengue cases in each division. We observed a major geographical shift of Dengue cases from the capital city Dhaka to different districts of Bangladesh with a higher incidence of dengue in the southern division of Bangladesh, influenced by temperature and urbanization.

Computer-aided detection (CAD) was recently recommended by the WHO for TB screening and triage based on several evaluations, but unlike traditional diagnostic tests, software versions are updated frequently and require constant evaluation. Since then, newer versions of two of the evaluated products have already been released. We used a case control sample of 12,890 chest X-rays to compare performance and model the programmatic effect of upgrading to newer versions of CAD4TB and qXR. We compared the area under the receiver operating characteristic curve (AUC), overall, and with data stratified by age, TB history, gender, and patient source. All versions were compared against radiologist readings and WHO’s Target Product Profile (TPP) for a TB triage test. Both newer versions significantly outperformed their predecessors in terms of AUC: CAD4TB version 6 (0.823 [0.816–0.830]), version 7 (0.903 [0.897–0.908]) and qXR version 2 (0.872 [0.866–0.878]), version 3 (0.906 [0.901–0.911]). Newer versions met WHO TPP values, older versions did not. All products equalled or surpassed the human radiologist performance with improvements in triage ability in newer versions. Humans and CAD performed worse in older age groups and among those with TB history. New versions of CAD outperform their predecessors. Prior to implementation CAD should be evaluated using local data because underlying neural networks can differ significantly. An independent rapid evaluation centre is necessitated to provide implementers with performance data on new versions of CAD products as they are developed.