Insight into Delta variant dominated second wave of COVID-19 in Nepal.

OBJECTIVE: To study the spreading nature of Delta variant (B.1.617.2) dominated COVID-19 in Nepal to help the policymakers assess and manage health care facilities and vaccination programs. METHODS: Deterministic mathematical models in the form of systems of ordinary differential equations were developed to describe the COVID-19 transmission in the high- and the low-risk regions of Nepal. The models were validated using the multiple data sets containing daily new cases in the whole country, the high-risk region, the low-risk region, and cases needing medical care, ICU, and ventilator. RESULTS: We found the reproduction number of Rt=4.2 at the beginning of the second wave, larger than the first wave (∼1.8 estimated previously), indicating that the transmissibility of Delta variant is higher than the wild-type circulated during the first wave. Model predicts that ∼5% of the COVID-19 cases were reported in Nepal, estimating the seroprevalence of ∼63.9% as of July 2021, consistent with the survey conducted by the Government of Nepal. The seroprevalence was expected to reach 94.46% by April 2022, among which ∼46% would have both infection and vaccination. The expected cases from September 2021 to April 2022 is 111,300, among which 11,890 people might need medical care, 3590 need ICU, and 953 need ventilators. The COVID-19 cases and medical care needs could be significantly reduced with proper implementation of vaccination and social distancing. CONCLUSIONS: The data-driven mathematical models are useful to assess control programs in resource-limited countries. The appropriate combination of vaccination and social distancing are necessary to keep the pandemic under-control and manage the medical care facilities in Nepal.

Accuracy and Glycemic Efficacy of Continuous Glucose Monitors in Critically Ill COVID-19 Patients: a Retrospective Study (preprint)

Background: Continuous Glucose Monitoring (CGM) is approved for insulin dosing decisions in the ambulatory setting, but not currently for inpatients. CGM has the capacity to reduce patient-provider contact in inpatients with coronavirus disease 2019 (COVID-19), thus potentially reducing in hospital virus transmission. However, there are sparse data on the accuracy and efficacy of CGM to titrate insulin doses in inpatients. Methods: Under an emergency use protocol, CGM (Dexcom G6) was used alongside standard point-of-care (POC) glucose measurements in patients critically ill from complications of COVID-19 requiring intravenous (IV) insulin. Glycemic control during IV insulin therapy was retrospectively assessed comparing periods with and without adjunctive CGM use. Accuracy metrics were computed and Clarke Error Grid analysis performed comparing CGM glucose values with POC measurements. Results: 24 critically ill patients who met criteria for emergency use of CGM resulted in 47333 CGM and 5677 POC glucose values. During IV insulin therapy, individuals' glycemic control improved when CGM was used (mean difference -30.2 mg/dL). Among 2194 matched CGM:POC glucose pairs a high degree of concordance was observed with a MARD of 14.8% and 99.5% of CGM:POC pairs falling in Zones A and B of the Clarke Error Grid. Conclusions: CGM use in critically ill COVID-19 patients improved glycemic control during IV insulin therapy. CGM glucose data were highly concordant with POC glucose during IV insulin therapy in critically ill patients suggesting that CGM could substitute for POC measurements in inpatients thus reducing patient-provider contact and mitigating infection transmission.

Transmission dynamics of COVID-19 in Nepal: Mathematical model uncovering effective controls.

While most of the countries around the globe are combating the pandemic of COVID-19, the level of its impact is quite variable among different countries. In particular, the data from Nepal, a developing country having an open border provision with highly COVID-19 affected country India, has shown a biphasic pattern of epidemic, a controlled phase (until July 21, 2020) followed by an outgrown phase (after July 21, 2020). To uncover the effective strategies implemented during the controlled phase, we develop a mathematical model that is able to describe the data from both phases of COVID-19 dynamics in Nepal. Using our best parameter estimates with 95% confidence interval, we found that during the controlled phase most of the recorded cases were imported from outside the country with a small number generated from the local transmission, consistent with the data. Our model predicts that these successful strategies were able to maintain the reproduction number at around 0.21 during the controlled phase, preventing 442,640 cases of COVID-19 and saving more than 1,200 lives in Nepal. However, during the outgrown phase, when the strategies such as border screening and quarantine, lockdown, and detection and isolation, were altered, the reproduction number raised to 1.8, resulting in exponentially growing cases of COVID-19. We further used our model to predict the long-term dynamics of COVID-19 in Nepal and found that without any interventions the current trend may result in about 18.76 million cases (10.70 million detected and 8.06 million undetected) and 89 thousand deaths in Nepal by the end of 2021. Finally, using our predictive model, we evaluated the effects of various control strategies on the long-term outcome of this epidemics and identified ideal strategies to curb the epidemic in Nepal.