The social divide of social distancing: Shelter-in-place behavior in Santiago during the Covid-19 pandemic

Voluntary shelter-in-place directives and lockdowns are the main nonpharmaceutical interventions that governments around the globe have used to contain the Covid-19 pandemic. In this paper, we study the impact of such interventions in the capital of a developing country, Santiago, Chile, that exhibits large socioeconomic inequality. A distinctive feature of our study is that we use granular geolocated mobile phone data to construct mobility measures that capture (1) shelter-in-place behavior and (2) trips within the city to destinations with potentially different risk profiles. Using panel data linear regression models, we first show that the impact of social distancing measures and lockdowns on mobility is highly heterogeneous and dependent on socioeconomic levels. More specifically, our estimates indicate that, although zones of high socioeconomic levels can exhibit reductions in mobility of around 50%-90% depending on the specific mobility metric used, these reductions are only 20%-50% for lower income communities. The large reductions in higher income communities are significantly driven by voluntary shelter-in-place behavior. Second, also using panel data methods, we show that our mobility measures are important predictors of infections: roughly, a 10% increase in mobility correlates with a 5% increase in the rate of infection. Our results suggest that mobility is an important factor explaining differences in infection rates between high- and low-incomes areas within the city. Further, they confirm the challenges of reducing mobility in lower income communities, where people generate their income from their daily work. To be effective, shelter-in-place restrictions in municipalities of low socioeconomic levels may need to be complemented by other supporting measures that enable their inhabitants to increase compliance. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Analytics Saves Lives During the COVID-19 Crisis in Chile

During the COVID-19 crisis, the Chilean Ministry of Health and the Ministry of Sciences, Technology, Knowledge and Innovation partnered with the Instituto Sistemas Complejos de Ingeniería (ISCI) and the telecommunications company ENTEL, to develop innovative methodologies and tools that placed operations research (OR) and analytics at the forefront of the battle against the pandemic. These innovations have been used in key decision aspects that helped shape a comprehensive strategy against the virus, including tools that (1) provided data on the actual effects of lockdowns in different municipalities and over time;(2) helped allocate limited intensive care unit (ICU) capacity;(3) significantly increased the testing capacity and provided on-the-ground strategies for active screening of asymptomatic cases;and (4) implemented a nationwide serology surveillance program that significantly influenced Chile's decisions regarding vaccine booster doses and that also provided information of global relevance. Significant challenges during the execution of the project included the coordination of large teams of engineers, data scientists, and healthcare professionals in the field;the effective communication of information to the population;and the handling and use of sensitive data. The initiatives generated significant press coverage and, by providing scientific evidence supporting the decision making behind the Chilean strategy to address the pandemic, they helped provide transparency and objectivity to decision makers and the general population. According to highly conservative estimates, the number of lives saved by all the initiatives combined is close to 3,000, equivalent to more than 5% of the total death toll in Chile associated with the pandemic until January 2022. The saved resources associated with testing, ICU beds, and working days amount to more than 300 million USD.

Infection with SARS-CoV-2 variant Gamma (P.1) in Chile increased ICU admission risk three to five-fold.

The 2021 wave of SARS-CoV-2 infection in Chile was characterized by an explosive increase in ICU admissions, which disproportionately affected individuals younger than 60 years. This second wave was also accompanied by an explosive increase in Gamma (P.1) variant detections and the massive vaccine rollout. We unveil the role the Gamma variant played in stressing the use of critical care, by developing and calibrating a queueing model that uses data on new onset cases and actual ICU occupancy, symptom's onset to ICU admission interval, ICU length-of-stay, genomic surveillance, and vaccine effectiveness. Our model shows that infection with the Gamma (P.1) variant led to a 3.5-4.7-fold increase in ICU admission for people younger than 60 years. This situation occurred on top of the already reported higher infection rate of the Gamma variant. Importantly, our results also strongly suggest that the vaccines used in Chile (inactivated mostly, but also an mRNA), were able to curb Gamma variant ICU admission over infections.

COVID-19 lateral flow IgG seropositivity and serum neutralising antibody responses after primary and booster vaccinations in Chile: a cross-sectional study.

BACKGROUND: By June 30, 2022, 92·6% of the Chilean population older than 18 years had received a full primary SARS-CoV-2 vaccine series, mostly with CoronaVac (Sinovac Biotech), and 78·4% had received a booster dose, mostly heterologous with BNT162b2 (Pfizer-BioNTech) and ChAdOx1 (AstraZeneca). We previously reported national seroprevalence data from lateral flow testing of IgG SARS-CoV-2 antibodies up to 16 weeks after primary vaccination. Our aim here was to study IgG seropositivity dynamics up to 30 weeks after primary vaccination and, in CoronaVac recipients, up to 26 weeks after booster vaccination, and to establish the correlation between lateral flow tests and neutralising antibody titres. METHODS: In this cross-sectional study, testing stations for SARS-CoV-2 IgG detection were selected and installed from March 12, 2021, in hotspots in 24 large Chilean cities, and were maintained until March 31, 2022. Individuals voluntarily approaching the testing stations were invited to perform a rapid lateral flow test via a finger prick and complete a questionnaire. Between Aug 12, 2021, and April 1, 2022, volunteers seeking medical care in the Mutual de Seguridad de la Cámara Chilena de la Construcción provided blood samples for lateral flow testing and neutralising antibody studies; inclusion criteria were age at least 18 years, history of complete primary vaccination series with CoronaVac, BNT162b2, or ChAdOx1, or no vaccine, and no previous COVID-19 diagnosis. We tested the difference in IgG positivity across time, and between primary and booster doses, in all eligible participants with complete records, controlling for age, gender, and comorbidities. We also assessed the predictive power of neutralising antibody titres and sociodemographic characteristics on the probability of IgG positive results using multivariable logistic regression. FINDINGS: Of 107 220 individuals recruited at the testing stations, 101 070 were included in our analysis (59 862 [59·2%] women and 41 208 [40·8%] men). 65 902 (65·2%) received primary vaccination series with CoronaVac, 18 548 (18·4%) with BNT162b2, and 606 (0·6%) with ChAdOx1, and 16 014 (15·8%) received no vaccine. Among the 61 767 individuals with a complete primary vaccination series with CoronaVac, 608 (1·0%) received a CoronaVac booster, 10 095 (16·3%) received a BNT162b2 booster, and 5435 (8·8%) received a ChAdOx1 booster. After ChAdOx1 primary vaccination, seropositivity peaked at week 5 after the second dose, occurring in 13 (92·9%, 95% CI 79·4-100·0) of 14 individuals. In participants who received a complete CoronaVac primary series, the decline in seropositivity stabilised at week 18 after the second dose (86 [44·7%, 95% CI 41·8-47·7] of 1087 individuals), whereas after receiving BNT162b2, seropositivity declined slightly by week 25 after the second dose (161 [94·2%, 90·6-97·7] of 171). A lower proportion of individuals who received the CoronaVac primary series and a homologous booster were seropositive (279 [85·6%, 95% CI 81·8-89·4] of 326) by weeks 2-18 than those who received a BNT162b2 booster (7031 [98·6%, 98·4-98·9] of 7128) or a ChAdOx1 booster (2893 [98·0%, 97·5-98·5] of 2953). The correlation between IgG positivity and log of the infectious dose in 50% of neutralising antibodies was moderate, with a sensitivity of 81·4% (95% CI 76·3-86·6) and specificity of 92·5% (73·3-100·0). INTERPRETATION: Dynamic monitoring of IgG positivity to SARS-CoV-2 can characterise antibody waning over time in the absence or presence of booster doses, providing relevant data for the design of vaccination strategies. The correlation between lateral flow test IgG titres and neutralising antibody concentrations suggests that they could be a quick and effective surveillance tool to measure protection against SARS-CoV-2. FUNDING: Instituto Sistemas Complejos de Ingeniería, Subsecretaría de Redes Asistenciales, Ministry of Health, Chile, and Mutual de Seguridad de la Cámara Chilena de la Construcción.

The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents.

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%-8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%-5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = -1.3%, range of -0.2% to -3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%-75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%-86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19.

The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%–8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%–5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = −1.3%, range of −0.2% to −3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%–75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%–86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19.

The diverse impacts of COVID-19 on electricity demand: The case of Chile

This paper analyzes the impacts of the first wave of COVID-19 (March 2020 - September 2020) on the electricity demand of different types of consumers in Chile, including residential, commercial, and industrial demand. We leverage data from 230 thousand smart meters of residential and commercial consumers in 32 communes of Santiago (the capital city of Chile), which allows us to investigate the evolution of their demands with an hourly temporal resolution. Additionally, we use demand data of large industrial consumers provided by the Chilean system operator to study the impact of the pandemic on different economic sectors. This paper demonstrates that the COVID-19 pandemic, and the associated containment measures, have featured a drastically different impact on the various types of consumers in Chile. In particular, we show that the demand of residential consumers has increased throughout the first wave, even when we isolate the effects of the pandemic from those related to weather. Furthermore, we study how these effects change in different communes of Santiago, contrasting our findings with the socio-economic levels of the population. In effect, we find different demand response patterns depending on the socio-economic background of consumers. We also show that commercial demand has significantly declined due to the containment measures implemented and that the hospitality and construction economic sectors have been the most affected in the country.

Dynamic IgG seropositivity after rollout of CoronaVac and BNT162b2 COVID-19 vaccines in Chile: a sentinel surveillance study.

BACKGROUND: By July 14, 2021, 81·3 % of adults (aged ≥18 years) in Chile had received a first SARS-CoV-2 vaccine and 72·3% had received a second SARS-CoV-2 vaccine, with the majority of people given Sinovac's inactivated CoronaVac vaccine (75·3% of vaccines dispensed) or Pfizer-BioNTech's mRNA BNT162b2 vaccine (20·9% of vaccines dispensed). Due to the absence of simultaneous real-world data for these vaccines, we aimed to compare SARS-CoV-2 IgG positivity between vaccines using a dynamic national monitoring strategy. METHODS: From March 12, 2021, 28 testing stations for SARS-CoV-2 IgG detection were installed in hotspots based on cellular-phone mobility tracking within the most populated cities in Chile. Individuals voluntarily approaching the testing stations were invited to do a lateral flow test by finger prick and respond to a questionnaire on sociodemographic characteristics, vaccination status (including type of vaccine if one was received), variables associated with SARS-CoV-2 exposure, and comorbidities. We compared the proportion of individuals testing positive for anti-SARS-CoV-2 IgG across sites by week since vaccination between recipients of CoronaVac and BNT162b2. Unvaccinated participants served as a control population and were matched to vaccinated individuals on the basis of date of presentation to the testing station, gender, and age group. Individuals were excluded from the analysis if they were younger than 18 years, had no declared gender, had an invalid IgG test result, had previously tested positive for SARS-CoV-2 infection on PCR, could not recall their vaccination status, or had been immunised against COVID-19 with vaccines other than CoronaVac or BNT162b2. Here, we report data collected up to July 2, 2021. FINDINGS: Of 64 813 individuals enrolled, 56 261 were included in the final analysis, of whom 33 533 (59·6%) had received at least one dose of the CoronaVac vaccine, 8947 (15·9%) had received at least one dose of the BNT162b2 vaccine, and 13 781 (24·5%) had not received a vaccine. SARS-CoV-2 IgG positivity during week 4 after the first dose of CoronaVac was 28·1% (95% CI 25·0-31·2; 220 of 783 individuals), reaching a peak of 77·4% (75·5-79·3; 1473 of 1902 individuals) during week 3 after the second dose. SARS-CoV-2 IgG positivity during week 4 after the first dose of the BNT162b2 vaccine was 79·4% (75·7-83·1; 367 of 462 individuals), increasing to 96·5% (94·9-98·1; 497 of 515 individuals) during week 3 after the second dose and remaining above 92% until the end of the study. For unvaccinated individuals, IgG seropositivity ranged from 6·0% (4·4-7·6; 49 of 810 individuals) to 18·7% (12·5-24·9; 28 of 150 individuals) during the 5 month period. Regression analyses showed that IgG seropositivity was significantly lower in men than women and in people with diabetes or chronic diseases for CoronaVac vaccine recipients (p<0·0001), and for individuals aged 60 years and older compared with people aged 18-39 years for both vaccines (p<0·0001), 3-16 weeks after the second dose. INTERPRETATION: IgG seropositivity was lower after CoronaVac than after BNT162b2 and declined over time since vaccination for CoronaVac recipients but not BNT162b2 recipients. Prolonged IgG monitoring will allow further evaluation of seropositivity overtime, providing data, in conjunction with effectiveness studies, for possible future re-assessment of vaccination strategies. FUNDING: Instituto Sistemas Complejos de Ingeniería and Ministerio de Salud Chile. TRANSLATION: For the Spanish translation of the abstract see Supplementary Materials section.

The effect of correlation and false negatives in pool testing strategies for COVID-19.

During the current COVID-19 pandemic, active testing has risen as a key component of many response strategies around the globe. Such strategies have a common denominator: the limited availability of diagnostic tests. In this context, pool testing strategies have emerged as a means to increase testing capacity. The efficiency gains obtained by using pool testing, derived from testing combined samples simultaneously, vary according to the spread of the SARS-CoV-2 virus in the population being tested. Motivated by the need for testing closed populations, such as long-term care facilities (LTCFs), where significant correlation in infections is expected, we develop a probabilistic model for settings where the test results are correlated, which we use to compute optimal pool sizes in the context of two-stage pool testing schemes. The proposed model incorporates the specificity and sensitivity of the test, which makes it possible to study the impact of these measures on both the expected number of tests required for diagnosing a population and the expected number and variance of false negatives. We use our experience implementing pool testing in LTCFs managed by SENAMA (Chile's National Service for the Elderly) to develop a simulation model of contagion dynamics inside LTCFs, which incorporates testing and quarantine policies implemented by SENAMA. We use this simulation to estimate the correlation of test results among collected samples when following SENAMA's testing guidelines. Our results show that correlation estimates are high in settings representative of LTCFs, which validates the use of the proposed model for incorporating correlation in determining optimal pool sizes for pool testing strategies. Generally, our results show that settings in which pool testing achieves efficiency gains, relative to individual testing, are likely to be found in practice. Moreover, the results show that incorporating correlation in the analysis of pool testing strategies both improves the expected efficiency and broadens the settings in which the technique is preferred over individual testing.

COVID-19: Short-term forecast of ICU beds in times of crisis.

By early May 2020, the number of new COVID-19 infections started to increase rapidly in Chile, threatening the ability of health services to accommodate all incoming cases. Suddenly, ICU capacity planning became a first-order concern, and the health authorities were in urgent need of tools to estimate the demand for urgent care associated with the pandemic. In this article, we describe the approach we followed to provide such demand forecasts, and we show how the use of analytics can provide relevant support for decision making, even with incomplete data and without enough time to fully explore the numerical properties of all available forecasting methods. The solution combines autoregressive, machine learning and epidemiological models to provide a short-term forecast of ICU utilization at the regional level. These forecasts were made publicly available and were actively used to support capacity planning. Our predictions achieved average forecasting errors of 4% and 9% for one- and two-week horizons, respectively, outperforming several other competing forecasting models.