A sustainable strategy for Open Streets in (post)pandemic cities

Cities world-wide have taken the opportunity presented by the COVID-19 pandemic to improve and expand pedestrian infrastructure, providing residents with a sense of relief and pursuing long-standing goals to decrease automobile dependence and increase walkability. So far, due to a scarcity of data and methodological shortcomings, these efforts have lacked the system-level view of treating sidewalks as a network. Here, we leverage sidewalk data from ten cities in three continents, to first analyse the distribution of sidewalk and roadbed geometries, and find that cities present an unbalanced distribution of public space, favouring automobiles at the expense of pedestrians. Next, we connect these geometries to build a sidewalk network –adjacent, but irreducible to the road network. Finally, we compare a no-intervention scenario with a shared-effort heuristic, in relation to the performance of sidewalk infrastructures to guarantee physical distancing. The heuristic prevents the sidewalk connectivity breakdown, while preserving the road network’s functionality.

Optimizing time-limited non-pharmaceutical interventions for COVID-19 outbreak control

Retrospective analyses of the non-pharmaceutical interventions (NPIs) used to combat the ongoing COVID-19 outbreak have highlighted the potential of optimizing interventions. These optimal interventions allow policymakers to manage NPIs to minimize the epidemiological and human health impacts of both COVID-19 and the intervention itself. Here, we use a susceptible–infectious–recovered (SIR) mathematical model to explore the feasibility of optimizing the duration, magnitude and trigger point of five different NPI scenarios to minimize the peak prevalence or the attack rate of a simulated UK COVID-19 outbreak. An optimal parameter space to minimize the peak prevalence or the attack rate was identified for each intervention scenario, with each scenario differing with regard to how reductions to transmission were modelled. However, we show that these optimal interventions are fragile, sensitive to epidemiological uncertainty and prone to implementation error. We highlight the use of robust, but suboptimal interventions as an alternative, with these interventions capable of mitigating the peak prevalence or the attack rate over a broader, more achievable parameter space, but being less efficacious than theoretically optimal interventions. This work provides an illustrative example of the concept of intervention optimization across a range of different NPI strategies. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

MO545INSIDE ANEMIA OF CKD: ESTIMATING THE IMPACT OF POLICY INTERVENTIONS ON ANAEMIA OF CKD IN THE USA BY MICROSIMULATION MODELLING

Background and Aims Anaemia is a common complication in patients with chronic kidney disease (CKD) and is associated with increased mortality, cardiovascular complications, reduced quality of life and increased use of healthcare resources. Mathematical modelling based on robust epidemiological and clinical data is a useful approach for predicting the future burden of disease and the impact of different intervention scenarios; this is important for health service planning. This analysis uses a microsimulation model, Inside ANEMIA of CKD, to predict the effects of a hypothetical intervention scenario that reduces the prevalence of anaemia of CKD on related healthcare costs in the USA from 2020 to 2025. Method A virtual cohort representing the US population was created within the Inside ANEMIA of CKD microsimulation model framework using demographics and epidemiological data drawn from the US Census Bureau, the Centers for Disease Control and Prevention, and the National Health and Nutrition Examination Survey. In the cohort, virtual individuals were ascribed an age–sex-stratified CKD status (defined by estimated glomerular filtration rate and albuminuria levels, as per international guidelines) and anaemia status (defined by haemoglobin level as mild, moderate or severe, as per WHO criteria) based on US prevalence data. Key comorbidities (type 2 diabetes, heart failure and hypertension) were also assigned, reflecting US-specific population statistics. Healthcare costs related to CKD and anaemia of CKD were taken from the published literature. The study modelled the effects on healthcare costs of a hypothetical intervention scenario in which the prevalence of moderate and severe anaemia is reduced by 20% per year from 2020 to 2025 compared with no intervention (baseline). In each scenario (i.e. intervention or baseline), the modelling analysis estimated healthcare costs related to CKD and anaemia (including inpatient, outpatient, pharmacy costs) for patients with moderate or severe anaemia of CKD. The model did not adjust for the potential costs of the intervention. Results Preliminary results predict that, with the hypothetical intervention, there could be 1.40 million fewer patients with moderate or severe anaemia of CKD in the USA in 2025 compared with no intervention (1.45 million versus 2.85 million). This represents a 49% reduction in cases of moderate or severe anaemia of CKD in 2025 with the intervention versus no intervention. The intervention is projected to lead to a reduction of approximately US$18 billion in annual direct healthcare costs in 2025 for patients with moderate or severe anaemia of CKD compared with no intervention (US$26 billion versus US$44 billion). Conclusion The Inside ANEMIA of CKD microsimulation model predicts that a hypothetical intervention which reduces the prevalence of moderate and severe anaemia of CKD would reduce direct healthcare costs. This suggests that interventions effective at reducing the prevalence of anaemia of CKD would help to reduce the economic burden on healthcare services.

MO549INSIDE ANEMIA OF CKD: MICROSIMULATION MODELLING OF THE IMPACT OF POLICY INTERVENTIONS ON ANAEMIA OF CKD IN CANADA

Background and Aims Anaemia is common in patients with chronic kidney disease (CKD) and is associated with increased mortality, cardiovascular complications, reduced quality of life and increased use of healthcare resources. Based on robust epidemiological and clinical data, mathematical modelling is a useful approach for predicting the future burden of disease and the effects of different intervention scenarios, which is essential for health service planning. This analysis uses a microsimulation model, Inside ANEMIA of CKD, to project the impact of a hypothetical intervention scenario that reduces the prevalence of anaemia of CKD on related healthcare costs in Canada from 2020 to 2025. Method A virtual cohort representing the Canadian population was created within the Inside ANEMIA of CKD microsimulation model framework using national demographics and epidemiological data drawn from Statistics Canada and a provincial renal database. In the cohort, virtual individuals were ascribed an age- and sex-stratified CKD status (defined by estimated glomerular filtration rate and albuminuria levels, as per international guidelines) and anaemia status (defined as mild, moderate or severe based on haemoglobin level, as per WHO criteria) based on Canadian prevalence data. Key comorbidities (type 2 diabetes, heart failure and hypertension) were also assigned, reflecting Canada-specific population statistics. Costs related to the treatment of CKD were taken from the published literature, and are shown in Canadian dollars (C$). This modelling analysis evaluated the effects on healthcare costs of a hypothetical intervention scenario in which the prevalence of moderate and severe anaemia is reduced by 20% per year from 2020 to 2025 compared with no intervention (baseline). In each scenario (i.e. intervention or baseline), the modelling analysis estimated CKD-related healthcare costs for patients with moderate or severe anaemia of CKD. The modelling analysis did not adjust for the potential costs of the intervention. Results Preliminary results predict that, with the hypothetical intervention, there could be approximately 435,000 fewer patients with moderate or severe anaemia of CKD in Canada in 2025 compared with no intervention (approximately 497,000 versus 932,000). The intervention is projected to lead to a reduction of C$4.4 billion in annual direct healthcare costs in 2025 for patients with moderate or severe anaemia of CKD compared with no intervention (C$9.1 billion versus C$13.5 billion), assuming that all eligible patients are diagnosed and treated. Conclusion The Inside ANEMIA of CKD microsimulation model predicts that a hypothetical intervention which reduces the prevalence of moderate and severe anaemia of CKD would produce reductions in direct healthcare costs. This suggests that interventions effective at reducing the prevalence of anaemia of CKD would help to reduce the economic burden on healthcare services.

How to Interact with a Fully Autonomous Vehicle: Naturalistic Ways for Drivers to Intervene in the Vehicle System while Performing Non-driving Related Tasks

Autonomous vehicle technology increasingly allows drivers to turn their primary attention to secondary tasks (e.g., eating or working). This dramatic behavior change thus requires new input modalities to support driver–vehicle interaction, which must match the driver’s in-vehicle activities and the interaction situation. Prior studies that addressed this question did not consider how acceptance for inputs was affected by the physical and cognitive levels experienced by drivers engaged in Non-driving Related Tasks (NDRTs) or how their acceptance varies according to the interaction situation. This study investigates naturalistic interactions with a fully autonomous vehicle system in different intervention scenarios while drivers perform NDRTs. We presented an online methodology to 360 participants showing four NDRTs with different physical and cognitive engagement levels, and tested the six most common intervention scenarios (24 cases). Participants evaluated our proposed seven natural input interactions for each case: touch, voice, hand gesture, and their combinations. Results show that NDRTs influence the driver’s input interaction more than intervention scenario categories. In contrast, variation of physical load has more influence on input selection than variation of cognitive load. We also present a decision-making model of driver preferences to determine the most natural inputs and help User Experience designers better meet drivers’ needs.

Mitigation of COVID-19 using social distancing of the elderly in Brazil: The vertical quarantine effects in hospitalizations and deaths

SummaryGovernments and epidemiologists have been proposing several mitigation strategies based on non-pharmaceutical interventions to reduce COVID-19 cases, hospitalizations, and deaths. In this work, we quantitatively compare the effects of elderly population (60 years old or more) selective isolation with a no isolation scenario using an adapted Susceptible - Exposed - Infectious - Removed (SEIR) compartmental model. For these simulated scenarios, we estimate the number of hospitalizations and deaths for different Brazilian cities, including those due to the lack of hospital beds. Our simulations show that, for São Paulo City, the isolation of the elderly would reduce demand for hospital beds by 9% and deaths by 16% compared to the no intervention scenario. Other Brazilian cities follow the same pattern, with median reductions of deaths ranging from 12-18%. We conclude that the social distancing of the elderly would be marginally effective and would not avoid health system collapse in several Brazilian cities.

Estimating the impact of tax policy interventions on the projected number and prevalence of adults with type 2 diabetes in Germany between 2020 and 2040

IntroductionAs a population-wide intervention, it has been proposed to raise taxes on unhealthy products to prevent diseases such as type 2 diabetes. In this study, we aimed to estimate the effect of tax policy interventions in 2020 on the projected prevalence and number of people with type 2 diabetes in the German adult population in 2040.Research design and methodsWe applied an illness-death model and the German Diabetes Risk Score (GDRS) to project the prevalence and number of adults with type 2 diabetes in Germany under a base case scenario and under a tax policy intervention scenario. For the base case scenario, we assumed constant age-specific incidence rates between 2020 and 2040. For the intervention scenario, we assumed a 50% price increase for sugar-sweetened beverages, tobacco and red meat products in the year 2020. Based on price elasticities, we estimated the impact on these risk factors alone and in combination, and calculated subsequent reductions in the age-specific and sex-specific GDRS. These reductions were used to determine reductions in the incidence rate and prevalence using a partial differential equation.ResultsCompared with the base case scenario, combined tax interventions in 2020 resulted in a 0.95 percentage point decrease in the prevalence of type 2 diabetes (16.2% vs 17.1%), which corresponds to 640 000 fewer prevalent cases of type 2 diabetes and a relative reduction by 6%.ConclusionsTaxation of sugar-sweetened beverages, tobacco products and red meat by 50% modestly lowered the projected number and prevalence of adults with type 2 diabetes in Germany in 2040. Raising taxes on unhealthy products as a stand-alone measure may not be enough to attenuate the future rise of type 2 diabetes.

Energy Security and Scenario Analysis of Province One of Federal Democratic Republic of Nepal

This paper presents the current energy security status of the Province One of Federal Democratic Republic Nepal using numerous indicators. In addition to that, with the development of five economic growth scenarios i.e. Normal growth (NOR) (5.80%), Low growth (LOW) (4.20%), High growth (HIG) (6.80%), Accelerated growth (ACC) (8.50%), and Intervention scenario (INT) on Normal growth scenario (NOR), the future energy mix and energy security indicators for the year 2040 are also calculated. The thesis also gives an overview of the variation/progress of the different indicators during the considered period of study (2017-2040). On the basis of compilation of data published by Water and Energy Commission Secretariat (WECS) and use of model developed in Long Range Energy Alternative Planning (LEAP), the study shows the increase in demand of the final energy consumption of province in Normal growth scenario to 101.8 million GJ at 2040 from 80.2 million GJ at 2017, whereby residential sector being the largest energy consuming sector with share of 68% in the total energy mix. However, the energy consumption increases to only 86.8 million GJ at the end year in the Intervention scenario. Similarly, the electricity consumption/demand increases by 120% and 390% in the normal and intervention economic growth scenario respectively. The study also shows that the, energy intensity in the intervention scenario is brought down to 4.44 GJ/USD1000 compared to that of 15 GJ/USD1000 in the base year. Also, the electricity consumption per capita is increased to 574 kWh from 116 kWh in the base year in INT scenario. Moreover, the GHG emission in the normal growth scenario at the end year will be 592.8 kg/Capita i.e. 59.5% increase from the base year. However, with the intervention scenario adopted, the GHG emission can be limited to only 385.2 kg/Capita in the end year, which is 8% increase from the base year value of 371.8 kg/Capita. However, these values are only achieved if the provincial government helps implementing federal government's plan of promoting the renewable energy systems and technologies instead of traditional fuel woods and fossil fuels system.

Isolation of infected people and their contacts is likely to be effective against many short-term epidemics

ABSTRACTBackgroundIsolation of infected people and their contacts may be an effective way to control outbreaks of infectious disease, such as influenza and SARS-CoV-2. Models can provide insights into the efficacy of contact tracing, coupled with isolating or quarantining at risk people.MethodsWe developed an agent-based model and simulated 15, 000 short term illnesses, with varying characteristics. For each illness we ran ten simulations on the following scenarios: (1) No tracing or isolation (None), (2) isolation of agents who have tested positive (Isolation), (3) scenario 2 coupled with minimal contact tracing and quarantine of contacts (Minimum), (4) scenario 3 with more effective contact tracing (Moderate), and (5) perfect isolation of agents who test positive and perfect tracing and quarantine of all their contacts (Maximum).ResultsThe median total infections of the Isolation, Minimum, Moderate and Maximum scenarios were 80%, 40%, 17% and 4% of the no intervention scenario respectively.ConclusionsIsolation of infected patients and quarantine of their contacts, even if moderately well implemented, is likely to substantially reduce the number of infections in an outbreak. Randomized controlled trials to confirm these results in the real world and to analyse the cost effectiveness of contact tracing and isolation during coronavirus and influenza outbreaks are warranted.

MODEL OPTIMASI PENGELOLAAN SAMPAH DI TPA

The increase in waste generation is a major problem especially for urban areas such as Jakarta with insufficient landfill capacity and an inefficient and environmentally sound waste management system. To produce an optimal, integrated and sustainable landfill management strategy, an analysis of the TPST Bantargebang waste management system is then formulated towards optimization of sustainable landfill management in environmental, financial, and social aspects through a system dynamics intervention scenario model of the TPST Bantargebang waste management system. Based on the descriptive analysis carried out on the latest waste management, 3 main issues are known, namely, landfill capacity almost fully occupied, methane gas emissions increment, and the possibility waste pickers integration to increase scavenging productivity. Simulations were carried out with a system dynamics model for the 2018-2023 period with BAU conditions and an intervention scenario with a reduction in landfill waste and a reduction in waste flow. The results of the scenario are: landfill can still be utilized until 2023; methane gas emissions decreased by an average of 23,50%; the increase in the Scavenger Production Ratio to the Landfill Waste Rate reached 134,58%. As a consequence of the intervention and the addition of waste treatment activities in the TPST Bantargebang, the operational cost per ton has increased up to 309,62%. This study concludes that the scenario of incoming waste reduction and existing landfill waste reduction planned by Material Recovery Facility (MRF) construction with scavenger involvement, compost processing efficiency improvement, construction of Waste to Energy (WtE) facilities in the form of incinerator, landfill mining, and reprofiling simultaneously.