Upregulation of Activation Induced Cell Markers (AIM) among Severe COVID-19 patients in Bangladesh (preprint)

COVID-19 caused by SARS-CoV-2 can develop the disease with different degree of clinical severity including fatality. In addition to antibody responses the antigen specific T cells may play a critical role in defining this protective immune response against this virus. As a part of a longitudinal cohort study in Bangladesh to investigate B and T cell specific immune responses, we sought to evaluate the activation induced cell marker (AIM) and the status of different immune cell subsets during infection. A total of 115 participants were analyzed in this study which included participants with asymptomatic, mild, moderate and severe clinical symptoms. In addition, healthy controls (19 in each group) were analysed. Specimens from participants collected during the pre-pandemic period were also analyzed (n=10). Follow-up visits were conducted on day 7, 14, and 28 for all the cases since the enrollment (day 1). In this study 10 participants among the moderate and severe cases expired during the course of follow up. We observed a decrease in mucosa associated invariant T (MAIT) cell frequency on the initial days (day 1 and day 7) in comparison to later days of the COVID-19 infection. However, natural killer (NK) cells were found to be elevated in symptomatic patients just after the onset of disease compared to both asymptomatic patients and healthy individuals. Moreover, we found AIM+ (both OX40+ CD137+ and OX40+ CD40L+) CD4+ T cells to show significant increase in moderate and severe COVID-19 patients in response to SARS-CoV-2 peptides (specially spike peptide) compared to prepandemic controls, who are unexposed to SARS-CoV-2. Notably, we did not observe any significant difference in the CD8+ AIM markers (CD137+ CD69+), which indicates the exhaustion of CD8+ T cells during COVID-19 infection. These findings suggest that the patients who recovered from moderate and severe COVID-19 were able to mount a strong CD4+ T cell response against shared viral determinants that ultimately induced the T cells to mount further immune responses to SARS-CoV-2.

Time Trend Analysis of Tuberculosis Treatment While Using Digital Adherence Technologies—An Individual Patient Data Meta-Analysis of Eleven Projects across Ten High Tuberculosis-Burden Countries

Worldwide, non-adherence to tuberculosis (TB) treatment is problematic. Digital adherence technologies (DATs) offer a person-centered approach to support and monitor treatment. We explored adherence over time while using DATs. We conducted a meta-analysis on anonymized longitudinal adherence data for drug-susceptible (DS) TB (n = 4515) and drug-resistant (DR) TB (n = 473) populations from 11 DAT projects. Using Tobit regression, we assessed adherence for six months of treatment across sex, age, project enrolment phase, DAT-type, health care facility (HCF), and project. We found that DATs recorded high levels of adherence throughout treatment: 80% to 71% of DS-TB patients had ≥90% adherence in month 1 and 6, respectively, and 73% to 75% for DR-TB patients. Adherence increased between month 1 and 2 (DS-TB and DR-TB populations), then decreased (DS-TB). Males displayed lower adherence and steeper decreases than females (DS-TB). DS-TB patients aged 15–34 years compared to those >50 years displayed steeper decreases. Adherence was correlated within HCFs and differed between projects. TB treatment adherence decreased over time and differed between subgroups, suggesting that over time, some patients are at risk for non-adherence. The real-time monitoring of medication adherence using DATs provides opportunities for health care workers to identify patients who need greater levels of adherence support.

Estimating the contribution of subclinical tuberculosis disease to transmission – an individual patient data analysis from prevalence surveys (preprint)

Background Individuals with bacteriologically confirmed pulmonary tuberculosis disease (TB) that do not report symptoms (subclinical TB) represent around half of all prevalent cases of TB, yet their contribution to Mycobacterium tuberculosis ( Mtb ) transmission is unknown, especially compared to individuals who report symptoms at time of diagnosis (clinical TB). Relative infectiousness can be approximated by cumulative infections in household contacts, but such data are rare. Methods and Findings We reviewed the literature to identify studies where surveys of Mtb infection were linked to population surveys of TB disease. We collated individual population data for analysis and used literature on the relative durations of subclinical and clinical TB to estimate relative infectiousness through a cumulative hazard model, accounting for sputum-smear status. Relative prevalence of subclinical and clinical disease in high burden settings was used to estimate the contribution of subclinical TB to global Mtb transmission. We collated data on 414 index cases and 789 household contacts from three prevalence surveys (Bangladesh, Philippines, Viet Nam) and one case-finding trial in Viet Nam. The odds ratio of household infection prevalence was 1.2 (0.6-2.3, 95% Confidence Interval). Adjusting for duration of disease, we found a per-unit-time infectiousness of subclinical TB relative to clinical TB of 1.93 (0.62-6.18, 95% Prediction Interval (PrI)). 14 countries across Asia and Africa provided data on relative prevalence of subclinical and clinical TB, suggesting an estimated 68% (27-92%, 95% PrI) of global transmission is from subclinical TB. Conclusions Our results suggest that subclinical TB contributes substantially to transmission and needs to be diagnosed and treated for effective progress towards TB elimination.

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).