Rt Transmission of Covid 19 in New Mexico

Objective : Analysis of COVID-19 dynamics in New Mexico reveals spatial heterogeneity in transmission pat- terns and the effects of public health interventions. This study estimated the time-varying reproduction number (Rt) of COVID-19 across New Mexico’s 5 public health regions from 2020 to 2022 to assess temporal and geographic variation in epidemic intensity and evaluate the impact of statewide policies. Methods : We analyzed daily confirmed COVID-19 case counts in New Mexico and its five public health re- gions (February 2020 to April 2022). Poisson-distributed multiplier of 4 were applied to account for irregular and under-reporting. We used the R package EpiEstim to estimate the time-varying reproduction number (Rt) with 7-day sliding windows (2020–2022) and non-overlapping time windows corresponding to five major pol- icy changes (2020 only). Poisson regression was used to estimate incidence rate ratios (IRRs) between each public health region and the statewide average across three study years: 2020, 2021, and 2022. Results : Statewide Rt declined sharply following emergency declarations and mask mandates but rebounded during phased reopenings and variant-driven waves. The highest Rt (approximately 2.0) occurred in March–April 2020, while sustained subcritical transmission (Rt < 1) was observed after the November 2020 “Red-to- Green” framework. Urban counties (Bernalillo, Santa Fe, and Don˜a Ana) exhibited earlier and higher Rt peaks than rural areas, where transmission persisted longer but was less intense. IRRs varied across regions, reflecting spatial heterogeneity in epidemic magnitude and intervention effectiveness. Conclusions : Public health policies reducing interpersonal contact, such as stay-at-home orders and mask mandates, effectively curtailed COVID-19 transmission in New Mexico. Continuous Rt monitoring is essen- tial for guiding localized response strategies, as structural and behavioral differences between urban and rural communities shape epidemic trajectories.