Inter-provincial disparity of COVID-19 transmission and control in Nepal

Abstract

Despite the global efforts to mitigate the ongoing COVID-19 pandemic, the disease transmission and the effective controls still remain uncertain as the outcome of the epidemic varies from place to place. In this regard, the province-wise data from Nepal provides a unique opportunity to study the effective control strategies. This is because (a) some provinces of Nepal share an open-border with India, resulting in a significantly high inflow of COVID-19 cases from India; (b) despite the inflow of a considerable number of cases, the local spread was quite controlled until mid-June of 2020, presumably due to control policies implemented; and (c) the relaxation of policies caused a rapid surge of the COVID-19 cases, providing a multi-phasic trend of disease dynamics. In this study, we used this unique data set to explore the inter-provincial disparities of the important indicators, such as epidemic trend, epidemic growth rate, and reproduction numbers. Furthermore, we extended our analysis to identify prevention and control policies that are effective in altering these indicators. Our analysis identified a noticeable inter-province variation in the epidemic trend (3 per day to 104 per day linear increase during third surge period), the median daily growth rate (1 to 4% per day exponential growth), the basic reproduction number (0.71 to 1.21), and the effective reproduction number (maximum values ranging from 1.20 to 2.86). Importantly, results from our modeling show that the type and number of control strategies that are effective in altering the indicators vary among provinces, underscoring the need for province-focused strategies along with the national-level strategy in order to ensure the control of a local spread.

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

“…Such inter-regional disparity highlights that regional level policy, and thus regional level modeling, is needed for more effective control of the local-level outbreak. The inter-region discrepancy overserved in our estimated is consistent with the inter-provincial disparity identified in Pantha et al ( Pantha et al, 2021b ) during the first wave of COVID-19 in Nepal.…”

“…As expected, most of the cases (>80%) were recorded in high-risk region and it peaked about one month earlier than low-risk region. Such spatial disparity on the pandemic trend was also found in the previous study ( Pantha et al, 2021b ), which performed the province-wise analysis of the first wave of COVID-19 in Nepal. The increasing trend of the epidemics remained for the period of April-May 2021 in high-risk region and for the period of May-June 2021 in low-risk region.…”

District-Level Risk Factors for COVID-19 Incidence and Mortality in Nepal

“…The government aggressively initiated a border screening policy to quarantine people traveling to Nepal from abroad, and provincial governments put in place targeted action on quarantine facilities and travel protocols. Nepal has faced multiple epidemic waves, with three distinct surge periods of COVID-19 cases: low (20 May to 25 June 2020), medium (22 July to 20 September 2020), and high (post-16 September 2020); these waves were due to an increase in susceptible population flow following the border opening (~20 May 2020), lockdown ending (~21 July 2020), and countrywide travel opening (~20 September 2020), respectively [ 13 ]. There was a rapid increase in the number of confirmed COVID-19 cases following the lifting of travel restrictions in many districts.…”

“…The Nepalese government ended a country-wide lockdown on 21 July 2020 [ 13 ] and called for various preventive interventions on hand hygiene, health, and social distancing to be designed and implemented [ 17 ], possibly prioritizing areas at elevated risk. Washing hands with soap and running water is one of the best preventive measures to protect individuals and prevent the community from COVID-19 transmission [ 18 ].…”

“…In addition, population density and household crowding have emerged as important risk factors for COVID-19 transmission [ 8 , 9 , 19 ]. A previous study reported geographic variation in the pandemic trends in Nepal and suggested regional strategies along with the national-level strategy to control the local spread of COVID-19 [ 13 ]. Therefore, it is critical to understand the risk factors at the district level that are associated with widespread infection, severity of illness, and mortality.…”