An Agent-Based Model for COVID-19 in Bangladesh (preprint)

BackgroundThe COVID-19 pandemic, that has resulted in millions of deaths and hundreds of millions of cases worldwide, continues to affect the lives, health and economy of various countries including Bangladesh. Despite the high proportion of asymptomatic cases and relatively low mortality, the viruss spread had been a significant public health problem for densely populated Bangladesh. With the healthcare system at stress, understanding the disease dynamics in the unique Bangladesh context became essential to guide policy decisions. MethodsWith a goal to capture the COVID-19 disease dynamics, we developed two stochastic Agent-Based Models (ABMs) considering the key characteristics of COVID-19 in Bangladesh, which vastly differ from the developed countries. We have implemented our ABMs extending the popular (but often inadequate) SIR model, where the infected population is sub-divided into Asymptomatic, Mild Symptomatic and Severe Symptomatic populations. One crucial issue in Bangladesh is the lack of enough COVID-19 tests as well as unwillingness of people to do the tests resulting in much less number of official positive cases than the actual reality. Although not directly relevant to the epidemiological process, our model attempts to capture this crucial aspect while calibrating against official daily test-positive cases. Our first model, ABM-BD, divides the population into age-groups that interact among themselves based on an aggregated Contact Matrix. Thus ABM-BD considers aggregate agents and avoids direct agent level interactions as the number of agents are prohibitively large in our context. We also implement a scaled down model, ABM-SD, that is capable of simulating agent level interactions. ResultsABM-BD was quite well-calibrated for Dhaka: the Mean Absolute Percentage Error (MAPE) between official and forecasted cases was 1.845 approximately during the period between April 4, 2020 and March 31, 2021. After an initial model validation, we conducted a number of experiments - including retrospective scenario analysis, and hypothetical future scenario analysis. For example, ABM-BD has demonstrated the trade off between a strict lockdown with low infections and a relaxed lockdown with reduced burden on the economy. Leveraging the true agent level interaction capability of ABD-SD, we have also successfully analyzed the relative severity of different strains thereby (confidently) capturing the effect of different virus mutations. ConclusionsOur models have adequately captured the COVID-19 disease transmission dynamics in Bangladesh. This is a useful tool to forecast the impact of interventions to assist policymakers in planning appropriate COVID response. Our models will be particularly useful in a resource constrained setting in countries like Bangladesh where the population size is huge.

Surging COVID-19 in Bangladesh driven by B.1.351 variant (preprint)

A dramatic resurgence of COVID-19 cases and deaths in Bangladesh in March 2021 coincided with the SARS-CoV-2 B.1.351 (501Y.V2) variant of concern rapidly becoming the dominant circulating variant. Concurrently, increasing numbers of reinfections have been detected and the effective Reproductive number, Rt, has doubled, despite high levels of prior infection in Dhaka city. These data support the prediction that acquired immunity from past infection provides reduced protection against B.1.351, and highlights the major public health concern posed by immune escape variants, especially in populations where vaccination coverage remains low.

Modelling to inform the COVID-19 response in Bangladesh (preprint)

Background: Non-pharmaceutical interventions (NPIs) used to limit SARS-CoV-2 transmission vary in their feasibility, appropriateness and effectiveness in different contexts. In Bangladesh a national lockdown implemented after the first detected case in early March 2020 rapidly exacerbated poverty and was considered untenable long-term, whilst surging cases in 2021 warrant renewed NPIs. We examine potential outcomes and costs of NPIs considered appropriate and feasible to deploy in Dhaka over the course of the pandemic including challenges of compliance and scale up. Methods: We developed an SEIR model for application to Dhaka District, parameterised from literature values and calibrated to death data from Bangladesh. We discussed scenarios and parameterizations with policymakers using an interactive app, to guide modelling of lockdown and post-lockdown measures considered feasible to deliver; symptoms-based household quarantining and compulsory mask-wearing. We examined how testing capacity affects case detection and compared deaths, hospitalisations relative to capacity, working days lost from illness and NPI compliance, and cost-effectiveness. Results: Lockdowns alone were predicted to delay the first epidemic peak but were unable to prevent overwhelming of the health service and were extremely costly. Predicted impacts of post-lockdown interventions depended on their reach within communities and levels of compliance: symptoms-based household quarantining alone was unable to prevent hospitalisations exceeding capacity whilst mask-wearing could prevent overwhelming health services and be cost-effective given masks of high filtration efficiency. The modelled combination of these measures was most effective at preventing excess hospitalizations for both medium and high filtration efficiency masks. Even at maximum testing capacity, confirmed cases far underestimate total cases, with saturation limiting reliability for assessing trends. Recalibration to surging cases in 2021 suggests limited immunity from previous infections and the need to re-sensitize communities to increase mask wearing. Conclusions: Masks and symptoms-based household quarantining act synergistically to prevent transmission, and are cost-effective in mitigating impacts. Our interactive app was valuable in supporting decision-making in Bangladesh, where mask-wearing was mandated early, and community teams have been deployed to support household quarantining across Dhaka. This combination of measures likely contributed to averting the worst impacts of a public health disaster as predicted under an unmitigated epidemic, but delivering an effective response at scale has been challenging. Moreover, lack of protection to the B.1.351 variant means messaging to improve mask-wearing is urgently needed in response to surging cases.

Genomic and mobility data reveal mass population movement as a driver of SARS-CoV-2 dissemination and diversity in Bangladesh (preprint)

BackgroundNew data streams are being used to track the pandemic of SARS-CoV-2, including genomic data which provides insights into patterns of importation and spatial spread of the virus, as well as population mobility data obtained from mobile phones. Here, we analyse the emergence and outbreak trajectory of SARS-CoV-2 in Bangladesh using these new data streams, and identify mass population movements as a key early event driving the ongoing epidemic. MethodsWe sequenced complete genomes of 67 SARS-CoV-2 samples (March-July 2020) and combined this dataset with 324 genomes from Bangladesh. For phylogenetic context, we also used 68,000 GISAID genomes collected globally. We paired this genomic data with population mobility information from Facebook and three mobile phone operators. FindingsThe majority (85%) of the Bangladeshi sequenced isolates fall into either pangolin lineage B.1.36 (8%), B.1.1 (19%) or B.1.1.25 (58%). Bayesian time-scaled phylogenetic analysis predicted SARS-COV-2 first appeared in mid-February, through international introductions. The first case was reported on March 8th. This pattern of repeated international introduction changed at the end of March when three discrete lineages expanded and spread clonally across Bangladesh. The shifting pattern of viral diversity across Bangladesh is reflected in the mobility data which shows the mass migration of people from cities to rural areas at the end of March, followed by frequent travel between Dhaka and the rest of the country during the following months. InterpretationIn Bangladesh, population mobility out of Dhaka as well as frequent travel from urban hotspots to rural areas resulted in rapid country-wide dissemination of SARS-CoV-2. The strains in Bangladesh reflect the local expansion of global lineages introduced early from international travellers to and from major international travel hubs. Importantly, the Bangladeshi context is consistent with epidemiologic and phylogenetic findings globally. Bangladesh is one of the few countries in the world with a rich history of conducting mass vaccination campaigns under complex circumstances. Combining genomics and these new data streams should allow population movements to be modelled and anticipated rendering Bangladesh extremely well prepared to immunize citizens rapidly. Based on our genomics data and the countrys successful immunization history, vaccines becoming available globally will be suitable for implementation in Bangladesh while ongoing genomic surveillance is conducted to monitor for new variants of the virus. FundingGovernment of Bangladesh, Bill and Melinda Gates Foundation, Wellcome Trust. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThe emergence of SARS-CoV-2, leading to the COVID-19 pandemic, has motivated all countries in the world to obtain high resolution data on the virus. Globally over 300,000 strains have been sequenced and information made available in GISAID. Within the first 100 days of the emergence of SARS-CoV-2, genomic analysis from different countries led to the development of vaccines which have now reached market. Information on the prevailing genotypes of SARS-CoV-2 since introduction is needed in low and middle-income countries (LMICs), including Bangladesh, in order to determine the suitability of therapeutics and vaccines in the pipeline and help vaccine deployment. Added value of this studyWe sequenced SARS-CoV-2 genomes from strains that were prospectively collected during the height of the pandemic and combined these genomic data with mobility data to comprehensively describe i) how repeated international importations of SARS-CoV-2 were ultimately linked to nationwide spread, ii) 85% of strains belonged to the Pangolin lineages B.1.1, B.1.1.25 and B.1.36 and that similar mutation rates were observed as seen globally iii) the switch in genomic dynamics of SARS-CoV-2 coincided with mass migration out of cities to the rest of the country. We have assessed the contributions of population mobility on the maintenance and spread of clonal lineages of SARS-CoV-2. This is the first time these data types have been combined to look at the spread of this virus nationally. Implications of all the available evidenceSARS-CoV-2 genomic diversity and mutation rate in Bangladesh is comparable to strains circulating globally. Notably, the data on the genomic changes of SARS-CoV-2 in Bangladesh is reassuring, suggesting that immunotherapeutic and vaccines being developed globally should also be suitable for this population. Since Bangladesh already has extensive experience of conducting mass vaccination campaigns, such as the rollout of the oral Cholera vaccine, experience of developing and using new data streams will enable efficient and targeted immunization of the population in 2021 with COVID-19 vaccine(s).

Participatory syndromic surveillance as a tool for tracking COVID-19 in Bangladesh (preprint)

Limitations in laboratory diagnostic capacity and reporting delays have hampered efforts to mitigate and control the ongoing coronavirus disease 2019 (COVID-19) pandemic globally. To augment traditional lab and hospital-based surveillance, Bangladesh established a participatory surveillance system for the public to self-report symptoms consistent with COVID-19 through multiple channels. Here, we report on the use of this system, which received over 3 million responses within two months, for tracking the COVID-19 outbreak in Bangladesh. Although we observe considerable noise in the data and initial volatility in the use of the different reporting mechanisms, the self-reported syndromic data exhibits a strong association with lab-confirmed cases at a local scale. Moreover, the syndromic data also suggests an earlier spread of the outbreak across Bangladesh than is evident from the confirmed case counts, consistent with predicted spread of the outbreak based on population mobility data. Our results highlight the usefulness of participatory syndromic surveillance for mapping disease burden generally, and particularly during the initial phases of an emerging outbreak.