A statistical model of internet traffic.

PhDWe present a method to extract a time series (Number of Active Requests (NAR)) from web cache logs which serves as a transport level measurement of internet traffic. This series also reflects the performance or Quality of Service of a web cache. Using time series modelling, we interpret the properties of this kind of internet traffic and its effect on the performance perceived by the cache user. Our preliminary analysis of NAR concludes that this dataset is suggestive of a long-memory self-similar process but is not heavy-tailed. Having carried out more in-depth analysis, we propose a three stage modelling process of the time series: (i) a power transformation to normalise the data, (ii) a polynomial fit to approximate the general trend and (iii) a modelling of the residuals from the polynomial fit. We analyse the polynomial and show that the residual dataset may be modelled as a FARIMA(p, d, q) process. Finally, we use Canonical Variate Analysis to determine the most significant defining properties of our measurements and draw conclusions to categorise the differences in traffic properties between the various caches studied. We show that the strongest illustration of differences between the caches is shown by the short memory parameters of the FARIMA fit. We compare the differences revealed between our studied caches and draw conclusions on them. Several programs have been written in Perl and S programming languages for this analysis including totalqd.pl for NAR calculation, fullanalysis for general statistical analysis of the data and armamodel for FARIMA modelling

The Impact of Alzheimer's Disease on the Chinese Economy.

BACKGROUND: Recent increases in life expectancy may greatly expand future Alzheimer's Disease (AD) burdens. China's demographic profile, aging workforce and predicted increasing burden of AD-related care make its economy vulnerable to AD impacts. Previous economic estimates of AD predominantly focus on health system burdens and omit wider whole-economy effects, potentially underestimating the full economic benefit of effective treatment. METHODS: AD-related prevalence, morbidity and mortality for 2011-2050 were simulated and were, together with associated caregiver time and costs, imposed on a dynamic Computable General Equilibrium model of the Chinese economy. Both economic and non-economic outcomes were analyzed. FINDINGS: Simulated Chinese AD prevalence quadrupled during 2011-50 from 6-28 million. The cumulative discounted value of eliminating AD equates to China's 2012 GDP (US$8 trillion), and the annual predicted real value approaches US AD cost-of-illness (COI) estimates, exceeding US$1 trillion by 2050 (2011-prices). Lost labor contributes 62% of macroeconomic impacts. Only 10% derives from informal care, challenging previous COI-estimates of 56%. INTERPRETATION: Health and macroeconomic models predict an unfolding 2011-2050 Chinese AD epidemic with serious macroeconomic consequences. Significant investment in research and development (medical and non-medical) is warranted and international researchers and national authorities should therefore target development of effective AD treatment and prevention strategies

The impact of Covid-19, associated behaviours and policies on the UK economy: A computable general equilibrium model.

We estimate the potential impact of COVID-19 on the United Kingdom economy, including direct disease effects, preventive public actions and associated policies. A sectoral, whole-economy macroeconomic model was linked to a population-wide epidemiological demographic model to assess the potential macroeconomic impact of COVID-19, together with policies to mitigate or suppress the pandemic by means of home quarantine, school closures, social distancing and accompanying business closures. Our simulations indicate that, assuming a clinical attack rate of 48% and a case fatality ratio of 1.5%, COVID-19 alone would impose a direct health-related economic burden of £39.6bn (1.73% of GDP) on the UK economy. Mitigation strategies imposed for 12 weeks reduce case fatalities by 29%, but the total cost to the economy is £308bn (13.5% of GDP); £66bn (2.9% of GDP) of which is attributable to labour lost from working parents during school closures, and £201bn (8.8% of GDP) of which is attributable to business closures. Suppressing the pandemic over a longer period of time may reduce deaths by 95%, but the total cost to the UK economy also increases to £668bn (29.2% of GDP), where £166bn (7.3% of GDP) is attributable to school closures and 502bn (21.9% of GDP) to business closures. Our analyses suggest Covid-19 has the potential to impose unprecedented economic costs on the UK economy, and whilst public actions are necessary to minimise mortality, the duration of school and business closures are key to determining the economic cost. The initial economic support package promised by the UK government may be proportionate to the costs of mitigating Covid-19, but without alternative measures to reduce the scale and duration of school and business closures, the economic support may be insufficient to compensate for longer term suppression of the pandemic which could generate an even greater health impact through major recession

Will More of the Same Achieve Malaria Elimination? Results from an Integrated Macroeconomic Epidemiological Demographic Model.

Historic levels of funding have reduced the global burden of malaria in recent years. Questions remain, however, as to whether scaling up interventions, in parallel with economic growth, has made malaria elimination more likely today than previously. The consequences of "trying but failing" to eliminate malaria are also uncertain. Reduced malaria exposure decreases the acquisition of semi-immunity during childhood, a necessary phase of the immunological transition that occurs on the pathway to malaria elimination. During this transitional period, the risk of malaria resurgence increases as proportionately more individuals across all age-groups are less able to manage infections by immune response alone. We developed a robust model that integrates the effects of malaria transmission, demography, and macroeconomics in the context of Plasmodium falciparum malaria within a hyperendemic environment. We analyzed the potential for existing interventions, alongside economic development, to achieve malaria elimination. Simulation results indicate that a 2% increase in future economic growth will increase the US$5.1 billion cumulative economic burden of malaria in Ghana to US$7.2 billion, although increasing regional insecticide-treated net coverage rates by 25% will lower malaria reproduction numbers by just 9%, reduce population-wide morbidity by -0.1%, and reduce prevalence from 54% to 46% by 2034. As scaling up current malaria control tools, combined with economic growth, will be insufficient to interrupt malaria transmission in Ghana, high levels of malaria control should be maintained and investment in research and development should be increased to maintain the gains of the past decade and to minimize the risk of resurgence, as transmission drops

Integrating economic and health evidence to inform Covid-19 policy in low- and middle- income countries

Covid-19 requires policy makers to consider evidence on both population health and economic welfare. Over the last two decades, the field of health economics has developed a range of analytical approaches and contributed to the institutionalisation of processes to employ economic evidence in health policy. We present a discussion outlining how these approaches and processes need to be applied more widely to inform Covid-19 policy; highlighting where they may need to be adapted conceptually and methodologically, and providing examples of work to date. We focus on the evidential and policy needs of low- and middle-income countries; where there is an urgent need for evidence to navigate the policy trade-offs between health and economic well-being posed by the Covid-19 pandemic.</ns3:p

Palm oil and dietary change: Application of an integrated macroeconomic, environmental, demographic, and health modelling framework for Thailand

Palm oil is a cooking oil and food ingredient in widespread use in the global food system. However, as a highly saturated fat, palm oil consumption has been associated with negative effects on cardiovascular health, while large scale oil palm production has been linked to deforestation. We construct an innovative fully integrated Macroeconomic-Environmental-Demographic-health (MED-health) model to undertake integrated health, environmental, and economic analyses of palm oil consumption and oil palm production in Thailand over the coming 20 years (2016–2035). In order to put a health and fiscal food policy perspective on policy priorities of future palm oil consumption growth, we model the implications of a 54% product-specific sales tax to achieve a halving of future energy intakes from palm cooking oil consumption. Total patient incidence and premature mortality from myocardial infarction and stroke decline by 0.03–0.16% and rural-urban equity in health and welfare improves in most regions. However, contrary to accepted wisdom, reduced oil palm production would not be environmentally beneficial in the Thailand case, since, once established, oil palms have favourable carbon sequestration characteristics compared to alternative uses of Thai cropland. The increased sales tax also provokes mixed economic impacts: While real GDP increases in a second-best Thai tax policy environment, relative consumption-to-investment price changes may reduce household welfare over extended periods unless accompanied by non-distortionary government compensation payments. Overall, our holistic approach demonstrates that product-specific fiscal food policy taxes may involve important trade-offs between nutrition, health, the economy, and the environment

COVID-19 vaccination in Sindh province, Pakistan: A modelling study of health impact and cost-effectiveness

Background: Multiple Coronavirus Disease 2019 (COVID-19) vaccines appear to be safe and efficacious, but only high-income countries have the resources to procure sufficient vaccine doses for most of their eligible populations. The World Health Organization has published guidelines for vaccine prioritisation, but most vaccine impact projections have focused on high-income countries, and few incorporate economic considerations. To address this evidence gap, we projected the health and economic impact of different vaccination scenarios in Sindh Province, Pakistan (population: 48 million).Methods and findings: We fitted a compartmental transmission model to COVID-19 cases and deaths in Sindh from 30 April to 15 September 2020. We then projected cases, deaths, and hospitalisation outcomes over 10 years under different vaccine scenarios. Finally, we combined these projections with a detailed economic model to estimate incremental costs (from healthcare and partial societal perspectives), disability-adjusted life years (DALYs), and incremental cost-effectiveness ratio (ICER) for each scenario. We project that 1 year of vaccine distribution, at delivery rates consistent with COVAX projections, using an infection-blocking vaccine at $3/dose with 70$27.9 per DALY averted from the health system perspective. Under a broad range of alternative scenarios, we find that initially prioritising the older (65+) population generally prevents more deaths. However, unprioritised distribution has almost the same cost-effectiveness when considering all outcomes, and both prioritised and unprioritised programmes can be cost-effective for low per-dose costs. High vaccine prices ($10/dose), however, may not be cost-effective, depending on the specifics of vaccine performance, distribution programme, and future pandemic trends. The principal drivers of the health outcomes are the fitted values for the overall transmission scaling parameter and disease natural history parameters from other studies, particularly age-specific probabilities of infection and symptomatic disease, as well as social contact rates. Other parameters are investigated in sensitivity analyses. This study is limited by model approximations, available data, and future uncertainty. Because the model is a single-population compartmental model, detailed impacts of nonpharmaceutical interventions (NPIs) such as household isolation cannot be practically represented or evaluated in combination with vaccine programmes. Similarly, the model cannot consider prioritising groups like healthcare or other essential workers. The model is only fitted to the reported case and death data, which are incomplete and not disaggregated by, e.g., age. Finally, because the future impact and implementation cost of NPIs are uncertain, how these would interact with vaccination remains an open question.Conclusions: COVID-19 vaccination can have a considerable health impact and is likely to be cost-effective if more optimistic vaccine scenarios apply. Preventing severe disease is an important contributor to this impact. However, the advantage of prioritising older, high-risk populations is smaller in generally younger populations. This reduction is especially true in populations with more past transmission, and if the vaccine is likely to further impede transmission rather than just disease. Those conditions are typical of many low- and middle-income countries

International trade, dietary change, and cardiovascular disease health outcomes: Import tariff reform using an integrated macroeconomic, environmental and health modelling framework for Thailand

United Nations (UN) member states have, since 2011, worked to address the emerging global NCD crisis, but progress has, so far, been insufficient. Food trade policy is recognised to have the potential to impact certain major diet-related health and environmental outcomes. We study the potential for using import tariff protection as a health and environmental policy instrument. Specifically, we apply a rigorous and consistent Macroeconomic-Environmental-Demographic-health (MED-health) simulation model framework to study fiscal food policy import tariffs and dietary change in Thailand over the future 20 year period 2016-2035. We find that the existing Thai tariff structure, by lowering imports, lowers agricultural Land Use Change (LUC)-related GHG emissions and protects against cholesterol-related cardiovascular disease (CVD). This confirms previous evidence that food trade, measured by import shares of food expenditures and caloric intakes, is correlated with unhealthy eating and adverse health outcomes among importing country populations. A continued drive towards tariff liberalization and economic efficiency in Thailand may therefore come at the expense of reduced health and environmental sustainability of food consumption and production systems. Due to large efficiency losses, the existing tariff structure is, however, not cost-effective as an environmental or health policy instrument. However, additional simulations confirm that stylized 30% food sector import tariffs generally improve nutritional, clinical health, demographic, and environmental indicators across the board. We also find that diet-related health improvements can go hand-in-hand with increased Saturated Fatty Acid (SFA) intakes. Despite limited cost-effectiveness, policy makers from Thailand and abroad, including WHO, would therefore be well advised to consider targeted fiscal food policy tariffs as a potential intervention to maintain combined health and environmental sustainability, and to reconsider the specification of WHO dietary guidelines with their focus on SFA intake (rather than composition of fatty acid intake) targets

Stark choices: exploring health sector costs of policy responses to COVID-19 in low-income and middle-income countries.

OBJECTIVES: COVID-19 has altered health sector capacity in low-income and middle-income countries (LMICs). Cost data to inform evidence-based priority setting are urgently needed. Consequently, in this paper, we calculate the full economic health sector costs of COVID-19 clinical management in 79 LMICs under different epidemiological scenarios. METHODS: We used country-specific epidemiological projections from a dynamic transmission model to determine number of cases, hospitalisations and deaths over 1 year under four mitigation scenarios. We defined the health sector response for three base LMICs through guidelines and expert opinion. We calculated costs through local resource use and price data and extrapolated costs across 79 LMICs. Lastly, we compared cost estimates against gross domestic product (GDP) and total annual health expenditure in 76 LMICs. RESULTS: COVID-19 clinical management costs vary greatly by country, ranging between <0.1%-12% of GDP and 0.4%-223% of total annual health expenditure (excluding out-of-pocket payments). Without mitigation policies, COVID-19 clinical management costs per capita range from US$43.39 to US$75.57; in 22 of 76 LMICs, these costs would surpass total annual health expenditure. In a scenario of stringent social distancing, costs per capita fall to US$1.10-US$1.32. CONCLUSIONS: We present the first dataset of COVID-19 clinical management costs across LMICs. These costs can be used to inform decision-making on priority setting. Our results show that COVID-19 clinical management costs in LMICs are substantial, even in scenarios of moderate social distancing. Low-income countries are particularly vulnerable and some will struggle to cope with almost any epidemiological scenario. The choices facing LMICs are likely to remain stark and emergency financial support will be needed

COVID-19 vaccination in Sindh Province, Pakistan: A modelling study of health impact and cost-effectiveness.

BACKGROUND: Multiple Coronavirus Disease 2019 (COVID-19) vaccines appear to be safe and efficacious, but only high-income countries have the resources to procure sufficient vaccine doses for most of their eligible populations. The World Health Organization has published guidelines for vaccine prioritisation, but most vaccine impact projections have focused on high-income countries, and few incorporate economic considerations. To address this evidence gap, we projected the health and economic impact of different vaccination scenarios in Sindh Province, Pakistan (population: 48 million). METHODS AND FINDINGS: We fitted a compartmental transmission model to COVID-19 cases and deaths in Sindh from 30 April to 15 September 2020. We then projected cases, deaths, and hospitalisation outcomes over 10 years under different vaccine scenarios. Finally, we combined these projections with a detailed economic model to estimate incremental costs (from healthcare and partial societal perspectives), disability-adjusted life years (DALYs), and incremental cost-effectiveness ratio (ICER) for each scenario. We project that 1 year of vaccine distribution, at delivery rates consistent with COVAX projections, using an infection-blocking vaccine at $3/dose with 70$27.9 per DALY averted from the health system perspective. Under a broad range of alternative scenarios, we find that initially prioritising the older (65+) population generally prevents more deaths. However, unprioritised distribution has almost the same cost-effectiveness when considering all outcomes, and both prioritised and unprioritised programmes can be cost-effective for low per-dose costs. High vaccine prices ($10/dose), however, may not be cost-effective, depending on the specifics of vaccine performance, distribution programme, and future pandemic trends. The principal drivers of the health outcomes are the fitted values for the overall transmission scaling parameter and disease natural history parameters from other studies, particularly age-specific probabilities of infection and symptomatic disease, as well as social contact rates. Other parameters are investigated in sensitivity analyses. This study is limited by model approximations, available data, and future uncertainty. Because the model is a single-population compartmental model, detailed impacts of nonpharmaceutical interventions (NPIs) such as household isolation cannot be practically represented or evaluated in combination with vaccine programmes. Similarly, the model cannot consider prioritising groups like healthcare or other essential workers. The model is only fitted to the reported case and death data, which are incomplete and not disaggregated by, e.g., age. Finally, because the future impact and implementation cost of NPIs are uncertain, how these would interact with vaccination remains an open question. CONCLUSIONS: COVID-19 vaccination can have a considerable health impact and is likely to be cost-effective if more optimistic vaccine scenarios apply. Preventing severe disease is an important contributor to this impact. However, the advantage of prioritising older, high-risk populations is smaller in generally younger populations. This reduction is especially true in populations with more past transmission, and if the vaccine is likely to further impede transmission rather than just disease. Those conditions are typical of many low- and middle-income countries