arXiv subjects

Faizunnesa Khondaker

Publications and source records attributed to Faizunnesa Khondaker.

3 recordsLinked to original sources

arXiv2026

A Multipatch Model for Evaluating Interdivisional Connectivity and Vaccination Dynamics in the 2026 Bangladesh Measles Outbreak

The 2026 measles resurgence in Bangladesh motivates a spatial modelling framework that accounts for heterogeneous local transmission, vaccination activity, and interdivisional connectivity. We formulate a multipatch susceptible-vaccinated-infectious-recovered model for the eight administrative divisions of Bangladesh. Division-specific parameters are estimated from reported measles cases \cite{DGHS} and smoothed vaccination data using bounded nonlinear least squares, with uncertainty quantified through 1000 parametric bootstrap replicates. The fitted divisional systems are then connected through compartment-specific movement and cross-patch infection coefficients adjusted for connectivity and the source division's relative reproduction number. The fitted model captures the main division-specific epidemic patterns. Estimated local reproduction numbers range from $1.1386$ in Khulna (95\% CI:$0.5703$--$1.3834$) to $4.3491$ in Dhaka (95\% CI: $1.4449$--$5.6307$), while Chattogram also exhibits high transmission potential ($\mathcal{R}_0^i=4.2275$; 95\% CI: $4.0019$--$4.3185$). Baseline spatial coupling increases the network-wide epidemic peak from $190.96$ to $313.20$ infectious individuals and more than doubles cumulative infections from $1086.42$ to $2345.35$. Relative to baseline coupling, 50\% and 80\% restrictions reduce the epidemic peak by $26.69\%$ and $34.37\%$, and cumulative infections by $25.07\%$ and $41.48\%$, respectively. Conversely, a 50\% increase in spatial coupling raises the peak by $30.14\%$ and cumulative infections by $23.54\%$. Although local transmission remains dominant in every division, cross-patch infection accounts for $28.83\%$ of new infections in Rajshahi at the baseline epidemic peak. Source-sink analysis identifies Dhaka as the dominant cross-patch source and Chattogram as a smaller secondary source.

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arXiv2026

Measles Resurgence in Bangladesh, 2026: A Situational Analysis for Urgent Public Health Response

\textbf{Background} Measles has resurged globally in the post-pandemic period as routine immunisation recovery remains below the two-dose threshold required to interrupt transmission. Bangladesh, previously nearing measles--rubella elimination, entered 2026 with widening coverage gaps, depleted vaccine stocks, and increasing numbers of missed children. We conducted a situation analysis to assess the scale, concentration, and programmatic implications of the outbreak. \textbf{Methods} We performed a rapid mixed-evidence review from 1--15 April 2026 using data from WHO, UNICEF, DGHS bulletins, PubMed/MEDLINE, ReliefWeb, SEARO updates, and Bangla-language media. Of 46 records screened, 19 were included. Analysis was based on aggregated, publicly available surveillance and programme data. \textbf{Findings} By 15 April 2026, Bangladesh reported 19{,}161 suspected cases, 2{,}973 confirmed cases, 166 suspected deaths, and 32 confirmed deaths across 58 districts since 15 March 2026. The outbreak was spatially concentrated: the top two divisions accounted for 56.5\% of cases (HHI = 0.217). Children under five comprised 81\% of cases, including 34\% infants under nine months. Vaccination status showed 72\% zero-dose and 16\% partially vaccinated cases. Coverage declined from 88.6\% to 86\% for MR1 and from 89\% to 80.7\% for MR2 (2019--2024), leaving about 20 million children vulnerable. \textbf{Interpretation} The resurgence reflects accumulated immunity gaps rather than vaccine failure, driven by subnational inequities and programme disruption. Urgent priorities include targeted vaccination campaigns, restoration of vitamin A supplementation, strengthening paediatric care capacity, and integrating real-time surveillance into outbreak response.

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arXiv2026

Climate-Driven Dengue Forecasting in Bangladesh: Division-Specific Feature-Set Design and Lag Structure

Bangladesh exhibits marked year-to-year variability in dengue, partly driven by meteorological fluctuations that shape \textit{Aedes} breeding-site persistence, mosquito development, and transmission. We exploit a contrast between Dhaka (consistently high burden) and Barishal (recently rising burden despite lower population density) and frame feature-set design and predictor structure as the main methodological contributions. Using monthly dengue data from DGHS \cite{DGHS} and meteorological data from World Weather Online \cite{Weather} for January 2022--October 2025, we compare four climate feature sets that vary wetness (rainy days vs.\ rainfall) and sunshine (sun days vs.\ sun hours), while temperature and humidity appear in all sets. We evaluate two predictor configurations: lagged climate covariates only, and lagged climate covariates plus 1-month lagged dengue incidence ($Y_{t-1}$). Climate lags (0--4 months) are applied in correlation and forecasting. Both divisions show similar delayed associations: rainfall metrics peak positively near a 2-month lag, humidity near a 1-month lag, sunshine metrics are most negative around a 2-month lag, and temperature is weakly positive at longer lags. We then benchmark MPR, ANN, XGBoost, and SARIMAX across all sets. Best performance differs: Dhaka favors ANN-1 with SET-1 (RMSE=2176.70, MAE=1282.00, MAPE=31.54\%), whereas Barishal favors SARIMAX(0,1,1)(1,0,0,12) with SET-2 (RMSE=817.56, MAE=717.78, MAPE=39.96\%). Analyses use consistent monthly aggregation and division-specific tuning.

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