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Prevention of household transmission crucial to stop the catastrophic spread of COVID-19 in cities

After weeks under lockdown, metropolitan areas fighting the spread of COVID-19 aim to balance public health goals with social and economic standards for well-being. Mathematical models of disease transmission seeking to evaluate mitigation strategies must assess the possible impacts of social distancing, economic lockdowns and other measures. However, obscure relations between model parameters and real-world phenomena complicate such analyses. Here, we use a high-resolution metapopulation model of Guadalajara (GDL, Western Mexico) to represent daily mobility patterns driven by economic activities and their relation to epidemic growth. Given the prominence of essential activities in the city's economy, we find that strategies aiming to mitigate the risk of out-of-home interactions are insufficient to stop the catastrophic spread of COVID-19. Using baseline reproduction numbers R0 = [2.5, 3.0] in the absence of interventions, our simulations suggest that household transmission alone can make Rt ~ 1, and is estimated to drive 70 {+/-} 15% of current epidemic growth. This sets an upper bound for the impact of mobility-based interventions, which are unlikely to lower Rt below 1.3 and must be complemented with aggressive campaigns for early case detection and isolation. As laboratory testing and health services become insufficient to meet demand in GDL and most other cities, we propose that cities facilitate guidelines and equipment to help people curb spreading within their own homes. Postponing these actions will increase their economic cost and decrease their potential returns.

Metropolitan areas around the world have implemented containment and mitigation 2 measures against the COVID-19 pandemic. Notably, non-essential activities have been 3 interrupted to limit the number of people exposed to the SARS-CoV-2 virus in schools, 4 retail stores, offices, and other places of interest. However, the economic burden exacted 5 by these measures has overwhelmed societies, leading to massive economic losses that 6 further complicate the fight against the coronavirus pandemic. Carefully validated 7 models of pandemic spread that incorporate realistic population mixing patterns could 8 help restart the economy while limiting the spread of the virus. 9 Recent studies have emphasized the potential benefits of aggressive testing 10 campaigns aimed to find and isolate symptomatic and asymptomatic carriers [1][2][3][4][5], but 11 these measures require a significant amount of resources, present logistical challenges, 12 and become increasingly unattainable in developing nations as incidence soars across 13 metropolitan areas. Although mathematical models of disease transmission can help 14 assess the potential effects of mitigation strategies, the interpretation and 15 communication of simulation results to the broader public is far from trivial [6,7]. In 16 most models, public health interventions and reactive behavior are represented by 17 time-dependent variables that are related to the epidemic's reproduction number R t , 18 defined as the number of secondary cases caused by a single patient who becomes 19 infective at time t [8,9]. However, without a realistic integration of city-specific 20 demographics and mixing patterns, mathematical models can only assume the relations 21 between mitigation strategies and epidemic growth [10]. 22 In this letter, we develop and evaluate a metapopulation model that incorporates 23 high-resolution census and economic data to replicate economic lockdowns and their 24 relations to COVID-19 transmission in metropolitan areas. Using the Guadalajara 25 Metropolitan Area (GDL, 2020 population estimated at 4.9 million inhabitants) as an 26 example, we find that mitigation strategies that aim to prevent disease spreading in 27 worksites, schools, and public spaces are unlikely to lower R t below 1.3. However, we 28 estimate that infections within residential settings can explain more than 55% of 29 epidemic growth during economic lockdowns. This suggests that catastrophic spread of 30 COVID-19 can only be avoided with mixed strategies that aim to prevent disease 31 transmission within households and public spaces. As hospital demand soars throughout 32 the world's cities and the majority of patients go untested, we recommend that 33 governments strive to isolate all symptomatic patients even without laboratory 34 confirmation. Authorities unable to setup designated areas for case isolation must then 35 provide citizens with equipment and guidelines for effective self-isolation at home. to date and 1292 that remain unresolved (13.4% positivity rate), the state of Jalisco is 41 thought to have a disproportionately low incidence despite its social, cultural and economic prominence in Mexico. Although confirmed case counts can produce 43 misleading comparisons between outbreaks in different regions due to differences in 44 testing rates, daily series of COVID-19 deaths comprise 100 deaths in GDL (2.3 deaths 45 per 100,000 people) out of 10,637 deaths nationwide (8.4 deaths per 100,000 people).

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The first case of COVID-19 in Jalisco was confirmed on March 11; the next day, 47 non-pharmaceutical measures to curb the spread of COVID-19 began in GDL, when all 48 large public events were cancelled (www.jalisco.gob.mx/es/prensa/noticias/102580).

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The Jalisco State Government later shut the city's schools and universities on March 17 50 and issued shelter-in-place orders on March 20, bringing non-essential activities to a 51 partial halt. Furthermore, the use of facemasks in public spaces was made mandatory 52 on April 20. A partial lift of shelter-in-place orders is set to begin on June 1 as 53 worksites adopt social distancing measures to prevent spreading among their customers 54 and employees.

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Model structure 56 Our model uses 2010 Census data [11] to distribute N = 4.3 million inhabitants across 57 1580 triangular neighborhoods describing the city region [12]. Each neighborhood Ω j 58 defines a metapopulation of size n j and is characterized by demographic and economic 59 variables used to infer the daily mobility habits of its residents ( Fig. 1.a). People in 60 each metapopulation are distributed throughout nine epidemiological compartments to 61 keep track of the natural history of COVID-19 ( Fig. 1 Exposure to SARS-CoV-2 in our model occurs through one of two mechanisms 63 described in Equation (2). The first term β l nj S j Φ j uses a weighted sum Φ j (Eq. 6) of 64 the number of residents in Ω j within each infective stage of COVID-19 and represents 65 homogeneous mixing within each neighborhood. Thus, the transmission potential β l 66 controls the frequency of infection within households and between neighbors.
Remote transmission of COVID-19 at places of interest is represented by the second 68 term in the right hand side of Equation 2. Under the formalism of distributed 69 contacts [13,14], this term uses origin-destination matrices P jk (t) to estimate people's 70 likely travel habits. Given this information, a weighted sum Ψ k (t) (Eq. 7) of the 71 number of infectives visiting locations Ω k at time t represents the risk of infection for 72 susceptibles traveling to this area. Lastly, the effective number S j P jk (t)Ψ k (t) of 73 susceptible-infective pairs at Ω k is divided by the total number of visitors v k (t) and 74 multiplied by the transmission potential of remote interactions β V . To investigate how 75 each term in Equation 2 contributes to epidemic growth, we can calculate their separate 76 contributions to the outbreak's reproductive number R t as Here, Θ is a coefficient that weighs the relative transmissivity and duration of 78 disease stages in Figure 1.b (Eq. 9). Meanwhile, Λ also incorporates isolation 79 parameters that reduce the frequency with which symptomatic and isolated patients quantify the severity of intervention strategies during this outbreak [15][16][17]. Our model 89 represents changes in the number of daily trips using Equation (4), which uses 90 sector-specific activity levels a c (t) to weigh the contributions P c jk made by each activity 91 sector c in the absence of interventions (Table 1, see Supplementary Information).

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Assuming that the city's baseline status is a c (t) = 1, economic lockdowns can be 93 represented by values a c (t) < 1, thus reducing the magnitude of P jk (t) and subsequent 94 rates of contact in places of interest. Furthermore, because activity sectors are unevenly 95 distributed throughout the city ( Fig. 2.a), different values of a c (t) can also transform 96 the topology of contact networks between the city's neighborhoods. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 7, 2020. . https://doi.org/10.1101/2020.06.05.20123711 doi: medRxiv preprint Index Activity sector Share of job market Contribution to daily mobility Industry 13% 6% Table 1. Places of interest were classified into five sectors to quantify their separate contributions to urban mobility and epidemic growth. Job data was obtained from the 2014 Economic Census [20], while daily mobility was quantified as the city-wide Trip Attraction Strength ( k T AS c k ) of establishments in each sector.
Baseline mobility patterns P c jk in each sector were inferred using the gravity model 98 in Equation (5). This approach uses the Trip Attraction Strength (T AS c k ) of 99 establishments in each sector to weigh the gravity model of Gonzalez et al. [18]. T AS c k 100 was formulated by Jakimavicius and Burinskiene [19] and is an estimate of the daily Directory [21]. A map of the individual contributions of registered worksites to T AS c k is 105 shown in Figure 2 Overall, the gravity model of Equation (5) describes travellers who seek to minimize 107 the distance ∆r jk between their place of residence Ω j and the places they visit Ω k .

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However, they're willing to travel far given the right incentive (areas with higher T AS c k ). 109 Coefficients d c j incorporate demographic information that describes the involvement of 110 metapopulations n j in sector c. For example, d 1 j is the fraction of people in Ω j who are 111 enrolled in educational programs and was obtained from census data, while d c j , c = 1 is a 112 function of the percentage of residents in Ω j who are economically active. By using 113 demographic information to refine inferred mobility patterns P jk (t), our framework for 114 disease transmission at places of interest (Eq. 2) can explicitly represent the impacts of 115 economic lockdowns and school closure as they first change mobility patterns.

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Mobility patterns used in our model are calibrated so that the number of people 117 v c k = j n j P c jk who visit a neighborhood Ω k in simulations matches the census estimate 118 T AS c k (Fig. 2). Because the mean of ratios v c k /T AS c k is within 0.05 of 1 for all sectors, 119 we consider that our representation of daily mobility can truthfully represent the 120 relative roles of each activity sector in the city's transportation network. Notice that 121 activity sectors make contributions to daily mobility k T AS c k that are 122 disproportionate to number of people that they employ (Table 1). This is caused by 123 secondary visitors like students, customers and suppliers whose presence is necessary to 124 sustain activities in each sector [19]. This implies that business closure in each sector 125 will come at its own economic cost but also yield different public health impacts. For 126 example, the closure of industrial activities in our model is least efficient in our model, 127 as it can affect up to 13% of jobs but only prevent 6% of daily trips.

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Activity levels a c (t) in our simulations were set using Google COVID-19 Mobility assumptions based on current policy. Simulations were initialized by the introduction of 135 1/(1 − ρ a ) people into exposed groups for each one of the 66 confirmed cases that were 136 identified as imported from other countries into the State of Jalisco. Imported cases on 137 their dates of arrival to the state of Jalisco, and transmission potentials β l , β V were 138 calibrated to reproduce weekly mortality data and produce a baseline reproduction 139 number 2.5 ≤ R 0 ≤ 3.0 before interventions (Fig. 3). The infection fatality rate (IFR) 140 was set to 0.7%, meaning that 7 out of every 1000 people exposed to SARS-CoV-2 in 141 our simulations die, while the rest recover 1/κ 1 + 1/κ 2 + 1/γ 1 = 12 days after exposure 142 (all model parameters are shown in Table S1). Starting on April 20, we assume that the 143 June 5, 2020 6/15 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 7, 2020. Google's mobility reports (a) to infer temporal changes in the reproduction number R t (b, Eq. 3). As mobility restrictions become more severe, epidemic growth is increasingly driven by residential transmission (c). Starting on April 20, we represent the mandatory use of face covering in public spaces through a 50% reduction in the corresponding transmission potential β V . The combination of reduced activity levels a c (t) and a reduced β V set R t ∼ 1.4 and suffice to reproduce weekly mortality data in panel d.
These results suggest that, under current conditions, more than two thirds of COVID-19 transmission occurs within households and between neighbors (c).
Notice that increases in the basic reproduction number R 0 must be compensated by 146 changes in the percentage of infections that occur within households and between 147 neighbors (Fig. 3.c). For example, changing R 0 from 2.5 to 2.9 requires that local 148 transmission (β l ) represents 35% (instead of 50%) of epidemic growth in the absence of 149 an intervention. This happens because increasing the value of β V relative to β l amplifies 150 the effects of mobility-based interventions a c (t), allowing each simulation to match 151 observed growth rates. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 7, 2020. . https://doi.org/10.1101/2020.06.05.20123711 doi: medRxiv preprint that β l has not changed significantly since the beginning of this outbreak and estimate 156 that residential routes of transmission can explain more than 55% of current epidemic 157 growth (Fig. 3.c). We now use β V = 0.318, β l = 0.172 (R 0 ∼ 2.9) to demonstrate the 158 potential benefits of public health campaigns that aim to prevent COVID-19 159 transmission within households. 160 Reacting, fast and slow 161 We now use the disease transmission scheme in Equation (2) to simulate alternate 162 scenarios where mobility-based (a c (t)-based) interventions are combined with aggressive 163 strategies of case isolation (CI). Our model represents improved CI by changing the 164 values of ρ s and α, which were respectively set at ρ s = 0.1 d −1 and α = 1/3 d −1 in our 165 calibration runs (Fig. 3). Campaigns simulated below expand CI to reach 40% or 80% 166 (ρ s = 0.4 , 0.8) of symptomatic patients only 1/α = 2 days after symptom onset. While 167 isolated in group H j , symptomatic patients make reduced contributions to residential 168 (30% of baseline value) and remote (3% of baseline value) disease spreading.

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To assess the urgency of CI strategies, we evaluated the effects of deploying such 170 campaigns on June 1, July 1 and August 1. Likewise, and considering that economic 171 lockdowns are bound to be relaxed soon, we tested two alternate scenarios for urban 172 mobility. In the fast scenario ( Fig. 4.a,c), gradual reopening of the economy occurs 173 between June 1 and August 1, when economic activities reach their baseline values (Fig. 174  S1.a). The slow scenario ( Fig. 4.b,c) extends partial economic lockdowns until 175 September 1 and keeps schools closed through mid-August (Fig. S1.b), while basic 176 education sets a 1 (t) = 0.6 between June 1 and July 17 in the fast scenario.

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Simulation results of daily new cases in Figure 4 suggest that an extended economic 178 lockdown (Figs. 4.b,d) can delay disease spreading after June 1, but will only curb it 179 significantly when combined with widespread CI. Even with 28% CI starting on June 1, 180 the extended lockdown described by activity levels in Figure S1 could sustain more than 181 30,000 new daily infections for roughly 40 days starting in September (Fig. 4.b).

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Meanwhile, a fast reopening of economic activities would produce similar infection rates 183 3 weeks earlier ( Fig. 4.a). In contrast, the early adoption of stricter campaigns with 184 56% CI and extended school closure can produce constant growth (R t ∼ 1, Figs 4.c,d). 185 Postponing these measures by only one month could cause a two-fold increase in health 186 care demands throughout June, July and August (Fig. 5).  (Figs. 3.b,c, 6). . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted June 7, 2020. . https://doi.org/10.1101/2020.06.05.20123711 doi: medRxiv preprint Comparing economic lockdowns and widespread case isolation as COVID-19 mitigation strategies. Differences between the left (a,c) and right (b,d) panels are caused by activity levels a c (t) describing fast and slow paths to reopen the city's economy (Fig. S1). Simulation ensembles illustrate the effects of improving CI strategies to isolate 28% (a,b) or 56% (c,d) of all people exposed to SARS-CoV-2 on three different dates. Patients are isolated two days after symptoms begin, thus reducing patients' residential and remote transmission potentials to 10% and 2% their baseline values respectively (q H = 0.1, c H = 0.2). Gray shading shows periods when all schools are closed. Spatial dependence of the percentage of the population exposed to SARS-CoV-2 by September 1 according to simulations in Figure 4.d. These experiments assume that activity levels follow a slow reopening of the economy (Fig. S1) and that, after improved CI strategies begin, 80% of symptomatic patients (56% CI) are isolated within 2 days of symptom onset.
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The copyright holder for this preprint this version posted June 7, 2020. All such measures are reflected on the second term of Equation (2) but don't tackle 202 contagious interactions that occur between household members and other close contacts 203 (first term in Equation 2), which contribute as much as 80% of estimated transmission 204 under lockdown (Fig. 3). This is visualized in Figure 6, which separates the 205 contributions made to R t by residential transmission and activity sectors with and 206 without an economic lockdown when β V = 0.318, β l = 0.172.

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By reducing the remote transmission potential β V , the mandatory use of facemasks 208 in public spaces [22] can further reduce the contribution of remote interactions (Fig.   209 3.b). However, notice that essential activities account for roughly 29% of daily mobility 210 in GDL and cannot be fully suspended during an intervention. This sets an upper 211 bound for the effect of mobility-based strategies that, along with high rates of 212 residential transmission, makes contention unlikely without addressing the mechanisms 213 responsible for disease spreading within households. While lockdowns and social 214 distancing measures as implemented in Wuhan and Shanghai are thought to have 215 stopped the spread of COVID-19 [23], differences in public awareness and compliance, 216 as well as higher rates of case detection and isolation [24] may explain this difference. 217 Wang and coauthors highlight the use of facemasks and frequent disinfection within the 218 households of suspected and confirmed patients [25] as an effective complementary 219 measure to mobility-based interventions. case isolation (CI) as proposed by many studies [2,3] is unlikely to occur in GDL, as 224 current testing capacity in Mexico is increasingly being reserved for heavily symptomatic 225 patients. Thus, we consider that campaigns must aim to build public awareness about 226 the symptoms of COVID-19 for people to self-diagnose and self-isolate when they're 227 symptomatic or have interacted with someone who was. Likewise, guidelines for 228 effective self-isolation at home [25] must be heavily publicized and made readily 229 available to everyone, as a majority of cases in developing countries is likely to go 230 undetected and many people still doubt that COVID-19 threatens their own health [26]. 231 We conclude that urgent action is necessary to prevent COVID-19 transmission 232 within households in GDL and other cities within the early stages of this outbreak. As 233 of June 1, we estimate that 0.5-0.9% of the city's inhabitants have been exposed to 234 SARS-CoV-2, but such low levels of incidence can give the wrong impression that public 235 health strategies used to date are sufficient to contain the epidemic. However, early 236 adoption of widespread CI and other mitigation strategies that prevent disease 237 spreading within residential settings can amplify the effects of mobility-based strategies 238 and social distancing in public spaces (Fig. 5).

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The reality of COVID-19 in urban areas is one of drastic inequality in access to 240 healthcare and the ability to self-isolate. Our simulations don't account for all such 241 heterogeneities, which can largely impact the evolution of an infectious outbreak [27][28][29]. 242 While our estimates of the reproduction number R t (Fig. 3.b) are sensible to numerous 243 model parameters and the introduction of imported cases, the conclusion that household 244 transmission is playing an increasing role in epidemic growth (Fig. 4.c) follows from the 245 fact that current policy in GDL only targets spreading in public spaces. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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Reopen slow
Reopen fast S1 Appendix. The weighted presence Φ j (t) of infective groups within their own 262 neighborhoods Ω j is written as When infectives travel to locations Ω k , their bulk potential for disease transmission 264 in Equation (2) is given by Coefficients q E = 0.2, q A = 0.5, q H = 0.1 account for changes in the transmissibility 266 of COVID-19 throughout different stages of the disease. Likewise, c I = 0.7, c H = 0.2 are 267 isolation coefficients that introduce reductions in the relative mobility of individuals 268 when they become symptomatic or self-isolated.

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The number of visitors at a destination Ω k , as used in Equations (2) Coefficients Θ (Eq. 9) and Λ (Eq. 10) account for the duration and relative 271 transmissibility of patients within each disease stage, necessary to calculate the 272 reproduction number R t (Eq. 3).
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The copyright holder for this preprint this version posted June 7, 2020. . https://doi.org/10.1101/2020.06.05.20123711 doi: medRxiv preprint To separate the contributions R c t made to the reproduction number R t by different 274 economic sectors in Figure 6, we substituted infective mobility matrices P jk (t) for their 275 sector-specific counterparts a c (t)P c jk in Equation (3). Origin-destination matrices 276 P mk (t)for susceptibles and the form of v k (t) were unchanged.

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As defined in previous studies [12,19], the Trip Attraction Strength (T AS k ) of a 278 neighborhood Ω k is calculated as the weighted sum 279 T AS k = 3Re + 2.4Se + 1.9In + 1.3(Pr + St), where parameters Re, Se, In and Pr denote the number of jobs registered at Ω k by 280 retail, service, industrial and primary activity organizations respectively. Similarly, St is 281 the number of students enrolled at educational institutions inside the same area. We 282 used georeferenced employer records from the 2014 Economic Census [20] and the 2018 283 National Directory of Schools [21] to allocate workers and students throughout the city's 284 neighborhoods. NAICS (North American Industrial Classification System) activity 285 codes were used to assign employers across mobility categories in Equation (11)  . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted June 7, 2020. . https://doi.org/10.1101/2020.06.05.20123711 doi: medRxiv preprint