Comparative analysis of COVID-19 diagnoses and mortality among hospitalized indigenous and non-indigenous populations in Chile: 2020-2021.

BACKGROUND: Current literature presents mixed effects of the COVID-19 pandemic on Indigenous communities. We aim to highlight potential disparities and temporal shifts in both the impact of COVID-19 and vaccine uptake among hospitalized Indigenous populations in Chile. METHODS: We conducted an observational analysis utilizing 1,598,492 hospitalization records from 2020 to 2021 based on publicly accessible hospital discharge data spanning 65 healthcare facilities of medium and high complexity funded through the Diagnosis-Related Groups (DRG) mechanism in Chile, representing roughly 70% of the country's total hospitalizations. This was supplemented with publicly available municipal data on COVID-19 vaccinations and socio-demographic variables. We performed logistic regression analysis at 0.05 level of significance to assess the bivariate and multivariable association of Indigenous status with COVID-19 diagnosis and COVID-19 deaths among hospitalized populations. We also performed univariate and multiple linear regression to assess the association of COVID-19 vaccination rate and Indigenous status at the municipality level. In addition, we report the distribution of top 10 secondary diagnoses among hospitalized COVID-19 cases and deaths separately for Indigenous and non-Indigenous populations. RESULTS: Indigenous populations displayed lower adjusted odds for both COVID-19 diagnosis (OR: 0.76, 95% CI: 0.74, 0.77) and death (OR: 0.91, 95% CI: 0.85, 0.97) when compared to non-Indigenous groups. Notably, the adjusted odds ratio for COVID-19 diagnosis in Indigenous populations rose from 0.59 (95% CI: 0.57, 0.61) in 2020 to 1.17 (95% CI: 1.13, 1.21) in 2021. Factors such as the significantly higher median age and greater number of comorbidities in the non-Indigenous hospitalized groups could account for their increased odds of COVID-19 diagnosis and mortality. Additionally, our data indicates a significantly negative adjusted association between COVID-19 vaccination rates and the proportion of Indigenous individuals. CONCLUSION: Although Indigenous populations initially showed lower odds of COVID-19 diagnosis and mortality, a marked rise in diagnosis odds among these groups in 2021 underscores the urgency of targeted interventions. The observed negative association between the proportion of Indigenous populations and vaccination rates further underscores the necessity to tackle vaccine access barriers and work towards equitable distribution.

Modeling the Impact of Agricultural Mitigation Measures on the Spread of Sharka Disease in Sweet Cherry Orchards.

Sharka is a disease affecting stone fruit trees. It is caused by the Plum pox virus (PPV), with Myzus persicae being one of the most efficient aphid species in transmitting it within and among Prunus orchards. Other agricultural management strategies are also responsible for the spread of disease among trees, such as grafting and pruning. We present a mathematical model of impulsive differential equations to represent the dynamics of Sharka disease in the tree and vector population. We consider three transmission routes: grafting, pruning, and through aphid vectors. Grafting, pruning, and vector control occur as pulses at specific instants. Within the model, human risk perception towards disease influences these agricultural management strategies. Model results show that grafting with infected biological material has a significant impact on the spread of the disease. In addition, detecting infectious symptomatic and asymptomatic trees in the short term is critical to reduce disease spread. Furthermore, vector control to prevent aphid movement between trees is crucial for disease mitigation, as well as implementing awareness campaigns for Sharka disease in agricultural communities that provide a long-term impact on responsible pruning, grafting, and vector control.

A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile.

A computational approach is adapted to analyze the parameter identifiability of a compartmental model. The model is intended to describe the progression of the COVID-19 pandemic in Chile during the initial phase in early 2020 when government declared quarantine measures. The computational approach to analyze the structural and practical identifiability is applied in two parts, one for synthetic data and another for some Chilean regional data. The first part defines the identifiable parameter sets when these recover the true parameters used to create the synthetic data. The second part compares the results derived from synthetic data, estimating the identifiable parameter sets from regional Chilean epidemic data. Experiments provide evidence of the loss of identifiability if some initial conditions are estimated, the period of time used to fit is before the peak, and if a significant proportion of the population is involved in quarantine periods.

The Nicaraguan Pediatric Influenza Cohort Study, 2011-2019: Influenza Incidence, Seasonality, and Transmission.

BACKGROUND: Children account for a large portion of global influenza burden and transmission, and a better understanding of influenza in children is needed to improve prevention and control strategies. METHODS: To examine the incidence and transmission of influenza we conducted a prospective community-based study of children aged 0-14 years in Managua, Nicaragua, between 2011 and 2019. Participants were provided with medical care through study physicians and symptomatic influenza was confirmed by reverse-transcription polymerase chain reaction (RT-PCR). Wavelet analyses were used to examine seasonality. Generalized growth models (GGMs) were used to estimate effective reproduction numbers. RESULTS: From 2011 to 2019, 3016 children participated, with an average of ∼1800 participants per year and median follow-up time of 5 years per child, and 48.3% of the cohort in 2019 had been enrolled their entire lives. The overall incidence rates per 100 person-years were 14.5 symptomatic influenza cases (95% confidence interval [CI]: 13.9-15.1) and 1.0 influenza-associated acute lower respiratory infection (ALRI) case (95% CI: .8-1.1). Symptomatic influenza incidence peaked at age 9-11 months. Infants born during peak influenza circulation had lower incidence in the first year of their lives. The mean effective reproduction number was 1.2 (range 1.02-1.49), and we observed significant annual patterns for influenza and influenza A, and a 2.5-year period for influenza B. CONCLUSIONS: This study provides important information for understanding influenza epidemiology and informing influenza vaccine policy. These results will aid in informing strategies to reduce the burden of influenza.

Comparative epidemiology of five waves of COVID-19 in Mexico, March 2020-August 2022.

BACKGROUND: The Mexican Institute of Social Security (IMSS) is the largest health care provider in Mexico, covering about 48% of the Mexican population. In this report, we describe the epidemiological patterns related to confirmed cases, hospitalizations, intubations, and in-hospital mortality due to COVID-19 and associated factors, during five epidemic waves recorded in the IMSS surveillance system. METHODS: We analyzed COVID-19 laboratory-confirmed cases from the Online Epidemiological Surveillance System (SINOLAVE) from March 29th, 2020, to August 27th, 2022. We constructed weekly epidemic curves describing temporal patterns of confirmed cases and hospitalizations by age, gender, and wave. We also estimated hospitalization, intubation, and hospital case fatality rates. The mean days of in-hospital stay and hospital admission delay were calculated across five pandemic waves. Logistic regression models were employed to assess the association between demographic factors, comorbidities, wave, and vaccination and the risk of severe disease and in-hospital death. RESULTS: A total of 3,396,375 laboratory-confirmed COVID-19 cases were recorded across the five waves. The introduction of rapid antigen testing at the end of 2020 increased detection and modified epidemiological estimates. Overall, 11% (95% CI 10.9, 11.1) of confirmed cases were hospitalized, 20.6% (95% CI 20.5, 20.7) of the hospitalized cases were intubated, and the hospital case fatality rate was 45.1% (95% CI 44.9, 45.3). The mean in-hospital stay was 9.11 days, and patients were admitted on average 5.07 days after symptoms onset. The most recent waves dominated by the Omicron variant had the highest incidence. Hospitalization, intubation, and mean hospitalization days decreased during subsequent waves. The in-hospital case fatality rate fluctuated across waves, reaching its highest value during the second wave in winter 2020. A notable decrease in hospitalization was observed primarily among individuals ≥ 60 years. The risk of severe disease and death was positively associated with comorbidities, age, and male gender; and declined with later waves and vaccination status. CONCLUSION: During the five pandemic waves, we observed an increase in the number of cases and a reduction in severity metrics. During the first three waves, the high in-hospital fatality rate was associated with hospitalization practices for critical patients with comorbidities.

Investigating COVID-19 transmission and mortality differences between indigenous and non-indigenous populations in Mexico.

OBJECTIVES: Indigenous populations have been disproportionately affected during pandemics. We investigated COVID-19 mortality estimates among indigenous and non-indigenous populations at national and sub-national levels in Mexico. METHODS: We obtained data from the Ministry of Health, Mexico, on 2,173,036 laboratory-confirmed RT-PCR positive COVID-19 cases and 238,803 deaths. We estimated mortality per 1000 person-weeks, mortality rate ratio (RR) among indigenous vs. non-indigenous groups, and hazard ratio (HR) for COVID-19 deaths across four waves of the pandemic, from February 2020 to March 2022. We also assessed differences in the reproduction number (Rt). RESULTS: The mortality rate among indigenous populations of Mexico was 68% higher than that of non-indigenous groups. Out of 32 federal entities, 23 exhibited higher mortality rates among indigenous groups (P < 0.05 in 13 entities). The fourth wave showed the highest RR (2.40). The crude HR was 1.67 (95% CI: 1.62, 1.72), which decreased to 1.08 (95% CI: 1.04, 1.11) after controlling for other covariates. During the intense fourth wave, the Rt among the two groups was comparable. CONCLUSION: Indigenous status is a significant risk factor for COVID-19 mortality in Mexico. Our findings may reflect disparities in non-pharmaceutical (e.g., handwashing and using facemasks), and COVID-19 vaccination interventions among indigenous and non-indigenous populations in Mexico.

An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020-2021.

Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with Rt<1.0 as of October 31, 2021. On the forecasting front, the sub-epidemic model performs best at capturing the 30-day ahead COVID-19 trajectory compared to the Richards and generalized logistic growth model. Nevertheless, the spatial variability in the incidence rate patterns across different departments can be grouped into four distinct clusters. As the case incidence surged in July 2020, an increase in mobility patterns was also observed. On the contrary, a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued, indicating the pandemic fatigue in the country.

Geospatial Variability in Excess Death Rates during the COVID-19 Pandemic in Mexico: Examining Socio Demographic, Climate and Population Health Characteristics.

OBJECTIVES: This study examined how socio-demographic, climate and population health characteristics shaped the geospatial variability in excess mortality patterns during the COVID-19 pandemic in Mexico. METHODS: We used Serfling regression models to estimate all-cause excess mortality rates for all 32 Mexican states. The association between socio-demographic, climate, health indicators and excess mortality rates were determined using multiple linear regression analyses. Functional data analysis characterized clusters of states with distinct excess mortality growth rate curves. RESULTS: The overall all-cause excess deaths rate during the COVID-19 pandemic in Mexico until April 10, 2021 was estimated at 39.66 per 10 000 population. The lowest excess death rates were observed in southeastern states including Chiapas (12.72) and Oaxaca (13.42), whereas Mexico City had the highest rate (106.17), followed by Tlaxcala (51.99). We found a positive association of excess mortality rates with aging index, marginalization index, and average household size (P < 0.001) in the final adjusted model (Model R2=77%). We identified four distinct clusters with qualitatively similar excess mortality curves. CONCLUSION: Central states exhibited the highest excess mortality rates, whereas the distribution of aging index, marginalization index, and average household size explained the variability in excess mortality rates across Mexico.

Transmission dynamics and forecasts of the COVID-19 pandemic in Mexico, March-December 2020.

Mexico has experienced one of the highest COVID-19 mortality rates in the world. A delayed implementation of social distancing interventions in late March 2020 and a phased reopening of the country in June 2020 has facilitated sustained disease transmission in the region. In this study we systematically generate and compare 30-day ahead forecasts using previously validated growth models based on mortality trends from the Institute for Health Metrics and Evaluation for Mexico and Mexico City in near real-time. Moreover, we estimate reproduction numbers for SARS-CoV-2 based on the methods that rely on genomic data as well as case incidence data. Subsequently, functional data analysis techniques are utilized to analyze the shapes of COVID-19 growth rate curves at the state level to characterize the spatiotemporal transmission patterns of SARS-CoV-2. The early estimates of the reproduction number for Mexico were estimated between Rt ~1.1-1.3 from the genomic and case incidence data. Moreover, the mean estimate of Rt has fluctuated around ~1.0 from late July till end of September 2020. The spatial analysis characterizes the state-level dynamics of COVID-19 into four groups with distinct epidemic trajectories based on epidemic growth rates. Our results show that the sequential mortality forecasts from the GLM and Richards model predict a downward trend in the number of deaths for all thirteen forecast periods for Mexico and Mexico City. However, the sub-epidemic and IHME models perform better predicting a more realistic stable trajectory of COVID-19 mortality trends for the last three forecast periods (09/21-10/21, 09/28-10/27, 09/28-10/27) for Mexico and Mexico City. Our findings indicate that phenomenological models are useful tools for short-term epidemic forecasting albeit forecasts need to be interpreted with caution given the dynamic implementation and lifting of social distancing measures.

Characterizing all-cause excess mortality patterns during COVID-19 pandemic in Mexico.

BACKGROUND: Low testing rates and delays in reporting hinder the estimation of the mortality burden associated with the COVID-19 pandemic. During a public health emergency, estimating all cause excess deaths above an expected level of death can provide a more reliable picture of the mortality burden. Here, we aim to estimate the absolute and relative mortality impact of COVID-19 pandemic in Mexico. METHODS: We obtained weekly mortality time series due to all causes for Mexico, and by gender, and geographic region from 2015 to 2020. We also compiled surveillance data on COVID-19 cases and deaths to assess the timing and intensity of the pandemic and assembled weekly series of the proportion of tweets about 'death' from Mexico to assess the correlation between people's media interaction about 'death' and the rise in pandemic deaths. We estimated all-cause excess mortality rates and mortality rate ratio increase over baseline by fitting Serfling regression models and forecasted the total excess deaths for Mexico for the first 4 weeks of 2021 using the generalized logistic growth model. RESULTS: We estimated the all-cause excess mortality rate associated with the COVID-19 pandemic in Mexico in 2020 at 26.10 per 10,000 population, which corresponds to 333,538 excess deaths. Males had about 2-fold higher excess mortality rate (33.99) compared to females (18.53). Mexico City reported the highest excess death rate (63.54) and RR (2.09) compared to rest of the country (excess rate = 23.25, RR = 1.62). While COVID-19 deaths accounted for only 38.64% of total excess deaths in Mexico, our forecast estimate that Mexico has accumulated a total of ~ 61,610 [95% PI: 60,003, 63,216] excess deaths in the first 4 weeks of 2021. Proportion of tweets was significantly correlated with the excess mortality (ρ = 0.508 [95% CI: 0.245, 0.701], p-value = 0.0004). CONCLUSION: The COVID-19 pandemic has heavily affected Mexico. The lab-confirmed COVID-19 deaths accounted for only 38.64% of total all cause excess deaths (333,538) in Mexico in 2020. This reflects either the effect of low testing rates in Mexico, or the surge in number of deaths due to other causes during the pandemic. A model-based forecast indicates that an average of 61,610 excess deaths have occurred in January 2021.

COVID-19 case fatality risk by age and gender in a high testing setting in Latin America: Chile, March-August 2020.

BACKGROUND: Early severity estimates of coronavirus disease 2019 (COVID-19) are critically needed to assess the potential impact of the ongoing pandemic in different demographic groups. Here we estimate the real-time delay-adjusted case fatality rate across nine age groups by gender in Chile, the country with the highest testing rate for COVID-19 in Latin America. METHODS: We used a publicly available real-time daily series of age-stratified COVID-19 cases and deaths reported by the Ministry of Health in Chile from the beginning of the epidemic in March through August 31, 2020. We used a robust likelihood function and a delay distribution to estimate real-time delay-adjusted case-fatality risk and estimate model parameters using a Monte Carlo Markov Chain in a Bayesian framework. RESULTS: As of August 31, 2020, our estimates of the time-delay adjusted case fatality rate (CFR) for men and women are 4.16% [95% Credible Interval (CrI): 4.09-4.24%] and 3.26% (95% CrI: 3.19-3.34%), respectively, while the overall estimate is 3.72% (95% CrI: 3.67-3.78%). Seniors aged 80 years and over have an adjusted CFR of 56.82% (95% CrI: 55.25-58.34%) for men and 41.10% (95% CrI: 40.02-42.26%) for women. Results showed a peak in estimated CFR during the June peak of the epidemic. The peak possibly reflects insufficient laboratory capacity, as illustrated by high test positivity rates (33% positive 7-day average nationally in June), which may have resulted in lower reporting rates. CONCLUSIONS: Severity estimates from COVID-19 in Chile suggest that male seniors, especially among those aged ≥ 70 years, are being disproportionately affected by the pandemic, a finding consistent with other regions. The ongoing pandemic is imposing a high death toll in South America, and Chile has one of the highest reported mortality rates globally thus far. These real-time estimates may help inform public health officials' decisions in the region and underscore the need to implement more effective measures to ameliorate fatality.

Transmission dynamics and control of COVID-19 in Chile, March-October, 2020.

Since the detection of the first case of COVID-19 in Chile on March 3rd, 2020, a total of 513,188 cases, including ~14,302 deaths have been reported in Chile as of November 2nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile's incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96 (95% CI: 0.95, 0.98) as of November 2nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.

Transmission dynamics and control of COVID-19 in Chile, March-October, 2020.

Since the detection of the first case of COVID-19 in Chile on March 3rd, 2020, a total of 513188 cases, including ~14302 deaths have been reported in Chile as of November 2nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile's incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96( 95% CI: 0.95, 0.98) as of November 2nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.

Pesticide application, educational treatment and infectious respiratory diseases: A mechanistic model with two impulsive controls.

In this paper, we develop and analyze an SIS-type epidemiological-mathematical model of the interaction between pesticide use and infectious respiratory disease transmission for investigating the impact of pesticide intoxication on the spread of these types of diseases. We further investigate the role of educational treatment for appropriate pesticide use on the transmission dynamics. Two impulsive control events are proposed: pesticide use and educational treatment. From the proposed model, it was obtained that the rate of forgetfulness towards educational treatment is a determining factor for the reduction of intoxicated people, as well as for the reduction of costs associated with educational interventions. To get reduced intoxications, the population's fraction to which is necessary to apply the educational treatment depends on its individual effectiveness level and the educational treatments' forgetfulness rate. In addition, the turnover of agricultural workers plays a fundamental role in the dynamics of agrotoxic use, particularly in the application of educational treatment. For illustration, a flu-like disease with a basic reproductive number below the epidemic threshold of 1.0 is shown can acquire epidemic potential in a population at risk of pesticide exposure. Hence, our findings suggest that educational treatment targeting pesticide exposure is an effective tool to reduce the transmission rate of an infectious respiratory disease in a population exposed to the toxic substance.

Risk of death by age and gender from CoVID-19 in Peru, March-May, 2020.

Peru implemented strict social distancing measures during the early phase of the epidemic and is now experiencing one of the largest CoVID-19 epidemics in Latin America. Estimates of disease severity are an essential indicator to inform policy decisions about the intensity and duration of interventions needed to mitigate the outbreak. Here we derive delay-adjusted case fatality risks (aCFR) of CoVID-19 in a middle-income country in South America.We utilize government-reported time series of CoVID-19 cases and deaths in Peru stratified by age group and gender.As of May 25, 2020, we estimate the aCFR for men and women at 10.8% (95%CrI: 10.5-11.1%) and 6.5% (95%CrI: 6.2-6.8%), respectively, whereas the overall aCFR was estimated at 9.1% (95%CrI: 8.9-9.3%). Our results show that senior individuals have been the most severely affected by CoVID-19, particularly men, with an aCFR of nearly 60% for those aged 80- years. We also found that men have a significantly higher cumulative morbidity ratio across most age groups (proportion test, p-value< 0.001), with the exception of those aged 0-9 years.The ongoing CoVID-19 pandemic is generating a substantial mortality burden in Peru. Senior individuals, especially those older than 70 years, are being disproportionately affected by the CoVID-19 pandemic.

Spatial dynamics and the basic reproduction number of the 1991-1997 Cholera epidemic in Peru.

After being cholera free for over 100 years, Peru experienced an unprecedented epidemic of Vibrio cholerae O1 that began in 1991 and generated multiple waves of disease over several years. We developed a mechanistic transmission model that accounts for seasonal variation in temperature to estimate spatial variability in the basic reproduction number ([Formula: see text]), the initial concentration of vibrios in the environment, and cholera reporting rates. From 1991-1997, cholera spread following a multi-wave pattern, with weekly incidence concentrated during warm seasons. The epidemic first hit the coastal departments of Peru and subsequently spread through the highlands and jungle regions. The correlation between model predictions and observations was high (range in R2: 58% to 97%). Department-level population size and elevation explained significant variation in spatial-temporal transmission patterns. The overall R0 across departments was estimated at 2.1 (95% CI: 0.8,7.3), high enough for sustained transmission. Geographic-region level [Formula: see text] varied substantially from 2.4 (95% CI: 1.1, 7.3) for the coastal region, 1.9 (0.7, 6.4) for the jungle region, and 1.5 (0.9, 2.2) for the highlands region. At the department level, mean [Formula: see text] ranged from 0.8 to 6.9. Department-level [Formula: see text] were correlated with overall observed attack rates (Spearman ρ = 0.59, P = 0.002), elevation (ρ = -0.4, P = 0.04), and longitude (ρ = -0.6, P = 0.004). We find that both [Formula: see text] and the initial concentration of vibrios were higher in coastal departments than other departments. Reporting rates were low, consistent with a substantial fraction of asymptomatic or mild cases associated with the El Tor cholera biotype. Our results suggest that cholera vibrios, autochthonous to plankton in the natural aquatic environment, may have triggered outbreaks in multiple coastal locations along the Pacific coast of Peru. Our methodology could be useful to investigate multi-wave epidemics of cholera and could be extended to conduct near real-time forecasts and investigate the impact of vaccination strategies.

Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru: February 29th-March 30th, 2020.

The COVID-19 pandemic that emerged in Wuhan China has generated substantial morbidity and mortality impact around the world during the last four months. The daily trend in reported cases has been rapidly rising in Latin America since March 2020 with the great majority of the cases reported in Brazil followed by Peru as of April 15th, 2020. Although Peru implemented a range of social distancing measures soon after the confirmation of its first case on March 6th, 2020, the daily number of new COVID-19 cases continues to accumulate in this country. We assessed the early COVID-19 transmission dynamics and the effect of social distancing interventions in Lima, Peru. We estimated the reproduction number, R, during the early transmission phase in Lima from the daily series of imported and autochthonous cases by the date of symptoms onset as of March 30th, 2020. We also assessed the effect of social distancing interventions in Lima by generating short-term forecasts grounded on the early transmission dynamics before interventions were put in place. Prior to the implementation of the social distancing measures in Lima, the local incidence curve by the date of symptoms onset displays near exponential growth dynamics with the mean scaling of growth parameter, p, estimated at 0.9 (95%CI: 0.9,1.0) and the reproduction number at 2.3 (95% CI: 2.0, 2.5). Our analysis indicates that school closures and other social distancing interventions have helped slow down the spread of the novel coronavirus, with the nearly exponential growth trend shifting to an approximately linear growth trend soon after the broad scale social distancing interventions were put in place by the government. While the interventions appear to have slowed the transmission rate in Lima, the number of new COVID-19 cases continue to accumulate, highlighting the need to strengthen social distancing and active case finding efforts to mitigate disease transmission in the region.

Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru: February 29th-March 30th, 2020.

The COVID-19 pandemic that emerged in Wuhan China has generated substantial morbidity and mortality impact around the world during the last four months. The daily trend in reported cases has been rapidly rising in Latin America since March 2020 with the great majority of the cases reported in Brazil followed by Peru as of April 15th, 2020. Although Peru implemented a range of social distancing measures soon after the confirmation of its first case on March 6th, 2020, the daily number of new COVID-19 cases continues to accumulate in this country. We assessed the early COVID-19 transmission dynamics and the effect of social distancing interventions in Lima, Peru. We estimated the reproduction number, R, during the early transmission phase in Lima from the daily series of imported and autochthonous cases by the date of symptoms onset as of March 30th, 2020. We also assessed the effect of social distancing interventions in Lima by generating short-term forecasts grounded on the early transmission dynamics before interventions were put in place. Prior to the implementation of the social distancing measures in Lima, the local incidence curve by the date of symptoms onset displays near exponential growth dynamics with the mean scaling of growth parameter, p, estimated at 0.96 (95% CI: 0.87, 1.0) and the reproduction number at 2.3 (95% CI: 2.0, 2.5). Our analysis indicates that school closures and other social distancing interventions have helped slow down the spread of the novel coronavirus, with the nearly exponential growth trend shifting to an approximately linear growth trend soon after the broad scale social distancing interventions were put in place by the government. While the interventions appear to have slowed the transmission rate in Lima, the number of new COVID-19 cases continue to accumulate, highlighting the need to strengthen social distancing and active case finding efforts to mitigate disease transmission in the region.

Prior dengue virus infection and risk of Zika: A pediatric cohort in Nicaragua.

BACKGROUND: Zika virus (ZIKV) emerged in northeast Brazil in 2015 and spread rapidly across the Americas, in populations that have been largely exposed to dengue virus (DENV). The impact of prior DENV infection on ZIKV infection outcome remains unclear. To study this potential impact, we analyzed the large 2016 Zika epidemic in Managua, Nicaragua, in a pediatric cohort with well-characterized DENV infection histories. METHODS AND FINDINGS: Symptomatic ZIKV infections (Zika cases) were identified by real-time reverse transcription PCR and serology in a community-based cohort study that follows approximately 3,700 children aged 2-14 years old. Annual blood samples were used to identify clinically inapparent ZIKV infections using a novel, well-characterized serological assay. Multivariable Poisson regression was used to examine the relation between prior DENV infection and incidence of symptomatic and inapparent ZIKV infection. The generalized-growth method was used to estimate the effective reproduction number. From January 1, 2016, to February 28, 2017, 560 symptomatic ZIKV infections and 1,356 total ZIKV infections (symptomatic and inapparent) were identified, for an overall incidence of 14.0 symptomatic infections (95% CI: 12.9, 15.2) and 36.5 total infections (95% CI: 34.7, 38.6) per 100 person-years. Effective reproduction number estimates ranged from 3.3 to 3.4, depending on the ascending wave period. Incidence of symptomatic and total ZIKV infections was higher in females and older children. Analysis of the effect of prior DENV infection was performed on 3,027 participants with documented DENV infection histories, of which 743 (24.5%) had experienced at least 1 prior DENV infection during cohort follow-up. Prior DENV infection was inversely associated with risk of symptomatic ZIKV infection in the total cohort population (incidence rate ratio [IRR]: 0.63; 95% CI: 0.48, 0.81; p < 0.005) and with risk of symptomatic presentation given ZIKV infection (IRR: 0.62; 95% CI: 0.44, 0.86) when adjusted for age, sex, and recent DENV infection (1-2 years before ZIKV infection). Recent DENV infection was significantly associated with decreased risk of symptomatic ZIKV infection when adjusted for age and sex, but not when adjusted for prior DENV infection. Prior or recent DENV infection did not affect the rate of total ZIKV infections. Our findings are limited to a pediatric population and constrained by the epidemiology of the site. CONCLUSIONS: These findings support that prior DENV infection may protect individuals from symptomatic Zika. More research is needed to address the possible immunological mechanism(s) of cross-protection between ZIKV and DENV and whether DENV immunity also modulates other ZIKV infection outcomes such as neurological or congenital syndromes.

Differences in Transmission and Disease Severity Between 2 Successive Waves of Chikungunya.

Background: Chikungunya, an arboviral disease, caused massive epidemics in Central and South America in 2014-2016. In a prospective pediatric cohort study, we examined the introduction of chikungunya in a naive population and investigated transmission and clinical characteristics. Methods: Children presenting to the study health center with a chikungunya-like illness or undifferentiated fever were tested for chikungunya virus (CHIKV) infection by reverse transcriptase-polymerase chain reaction (RT-PCR) and serological assays. Inapparent CHIKV infections in the intervening year were determined by seroconversion in healthy blood samples collected annually. Results: A total of 4353 children participated in the cohort study from March 2014 to February 2016 during the 2 epidemic waves of chikungunya. A total of 539 cases of chikungunya were documented, for an incidence rate of 80.2 cases per 1000 person-years (95% confidence interval [CI]: 73.7, 87.2); and a total of 893 CHIKV infections were documented, for an incidence rate of 137.1 infections per 1000 person-years (95% CI: 128.4, 146.4). The seroprevalence of anti-CHIKV antibodies increased linearly with age, with seroprevalence of >45% in 14-year-old children at the end of Epidemic 2. Symptom presentation varied between the epidemics, with Epidemic 2 exhibiting both a higher symptomatic-to-inapparent ratio (1:1.20 in Epidemic 1 vs. 1:0.65 in Epidemic 2) and more severe clinical presentation among cases. The mean reproduction number was also greater in Epidemic 2 than in Epidemic 1. Conclusions: The intensity of transmission and severity of clinical presentation varied between the 2 epidemics, with higher transmission intensity associated with greater disease severity.