Mathematical Modeling of Virus Dynamics in Immunology

A simplified dynamical model of immune response to uncomplicated influenza virus infection is presented, which focuses on the control of the infection by the innate and adaptive immunity. Innate immunity is represented by interferon-induced resistance to infection of respiratory epithelial cells and by removal of infected cells by effector cells. Adaptive immunity is represented by virus-specific antibodies. Similar in spirit to the recent model of Bocharov & Romanyukha (Bocharov and Romanyukha, 1994), the model is constructed as a system of 10 ordinary differential equations with 27 parameters. In the first part, parameter values for the model are obtained either from published experimental data or by estimation based on fitting available data about the time course of IAV infection in a naïve host. Sensitivity analysis is performed on the model parameters. To account for the variability and speed of adaptation, a variable is introduced that quantifies the antigenic compatibility between the virus and the antibodies. It is found that for small initial viral load the disease progresses through an asymptomatic course, for intermediate value it takes a typical course with constant duration and severity of infection but variable onset, and for large initial viral load the disease becomes severe. The absence of antibody response leads to recurrence of disease and appearance of a chronic state with nontrivial constant viral load. In the second part, an ensemble model of immune response is developed, which consists of multiple ODE models that are identical in form but differ in parameter values. A probabilistic measure of goodness of fit of the ODE model is used to derive an a posteriori probability density on the space of parameter values. This probability density is sampled using the Metropolis Monte Carlo method and sampling is enhanced using parallel tempering algorithm. The ensemble model is employed to compute probabilistic estimates on trajectory of the immune response, duration of disease, maximum damage, likelihood of rebound in the disease and the probability of occurrence of superspreaders. The effectiveness of using antiviral drug to treat the infection is addressed and optimal treatment scenarios are discussed

Kinderbetreuung

Im Rahmen der vom Bundesministerium für Familie, Senioren, Frauen und Jugend (BMFSFJ) und vom Bundesministerium der Finanzen (BMF) in Auftrag gegebenen Gesamtevaluation von zentralen ehe- und familienbezogenen Leistungen wurden in der vorliegenden Studie die Auswirkungen öffentlich geförderter Kinderbetreuung auf Familien untersucht. In einer ex-post Analyse wurden die Effekte von Kinderbetreuung auf folgende familienpolitische Ziele identifiziert: Vereinbarkeit von Familie und Beruf/wirtschaftliche Stabilität und soziale Teilhabe von Familien, Steigerung der Geburtenrate/Realisierung von Kinderwünschen. Die Wirkungsanalysen wurden für Familien mit unter-dreijährigen Kindern (U3), mit drei- bis sechsjährigen Kindern (Ü3) sowie für Schulkinder (Ü6) separat durchgeführt. Im Anschluss wurden in Effizienzanalysen die Selbstfinanzierungsquoten des Ausbaus der Kindertagesbetreuung untersucht

Modelling cross-reactivity and memory in the cellular adaptive immune response to influenza infection in the host

The cellular adaptive immune response plays a key role in resolving influenza infection. Experiments where individuals are successively infected with different strains within a short timeframe provide insight into the underlying viral dynamics and the role of a cross-reactive immune response in resolving an acute infection. We construct a mathematical model of within-host influenza viral dynamics including three possible factors which determine the strength of the cross-reactive cellular adaptive immune response: the initial naive T cell number, the avidity of the interaction between T cells and the epitopes presented by infected cells, and the epitope abundance per infected cell. Our model explains the experimentally observed shortening of a second infection when cross-reactivity is present, and shows that memory in the cellular adaptive immune response is necessary to protect against a second infection.Comment: 35 pages, 12 figure

Measuring Coverage in MNCH:A Validation Study Linking Population Survey Derived Coverage to Maternal, Newborn, and Child Health Care Records in Rural China

Accurate data on coverage of key maternal, newborn, and child health (MNCH) interventions are crucial for monitoring progress toward the Millennium Development Goals 4 and 5. Coverage estimates are primarily obtained from routine population surveys through self-reporting, the validity of which is not well understood. We aimed to examine the validity of the coverage of selected MNCH interventions in Gongcheng County, China.We conducted a validation study by comparing women's self-reported coverage of MNCH interventions relating to antenatal and postnatal care, mode of delivery, and child vaccinations in a community survey with their paper- and electronic-based health care records, treating the health care records as the reference standard. Of 936 women recruited, 914 (97.6%) completed the survey. Results show that self-reported coverage of these interventions had moderate to high sensitivity (0.57 [95% confidence interval (CI): 0.50-0.63] to 0.99 [95% CI: 0.98-1.00]) and low to high specificity (0 to 0.83 [95% CI: 0.80-0.86]). Despite varying overall validity, with the area under the receiver operating characteristic curve (AUC) ranging between 0.49 [95% CI: 0.39-0.57] and 0.90 [95% CI: 0.88-0.92], bias in the coverage estimates at the population level was small to moderate, with the test to actual positive (TAP) ratio ranging between 0.8 and 1.5 for 24 of the 28 indicators examined. Our ability to accurately estimate validity was affected by several caveats associated with the reference standard. Caution should be exercised when generalizing the results to other settings.The overall validity of self-reported coverage was moderate across selected MNCH indicators. However, at the population level, self-reported coverage appears to have small to moderate degree of bias. Accuracy of the coverage was particularly high for indicators with high recorded coverage or low recorded coverage but high specificity. The study provides insights into the accuracy of self-reports based on a population survey in low- and middle-income countries. Similar studies applying an improved reference standard are warranted in the future

mHealth Series:mHealth project in Zhao County, rural China - Description of objectives, field site and methods

BACKGROUND: We set up a collaboration between researchers in China and the UK that aimed to explore the use of mHealth in China. This is the first paper in a series of papers on a large mHealth project part of this collaboration. This paper included the aims and objectives of the mHealth project, our field site, and the detailed methods of two studies. FIELD SITE: The field site for this mHealth project was Zhao County, which lies 280 km south of Beijing in Hebei Province, China. METHODS: We described the methodology of two studies: (i) a mixed methods study exploring factors influencing sample size calculations for mHealth–based health surveys and (ii) a cross–over study determining validity of an mHealth text messaging data collection tool. The first study used mixed methods, both quantitative and qualitative, including: (i) two surveys with caregivers of young children, (ii) interviews with caregivers, village doctors and participants of the cross–over study, and (iii) researchers’ views. We combined data from caregivers, village doctors and researchers to provide an in–depth understanding of factors influencing sample size calculations for mHealth–based health surveys. The second study, a cross–over study, used a randomised cross–over study design to compare the traditional face–to–face survey method to the new text messaging survey method. We assessed data equivalence (intrarater agreement), the amount of information in responses, reasons for giving different responses, the response rate, characteristics of non–responders, and the error rate. CONCLUSIONS: This paper described the objectives, field site and methods of a large mHealth project part of a collaboration between researchers in China and the UK. The mixed methods study evaluating factors that influence sample size calculations could help future studies with estimating reliable sample sizes. The cross–over study comparing face–to–face and text message survey data collection could help future studies with developing their mHealth tools

Does global progress on sanitation really lag behind water? An analysis of global progress on community- and household-level access to safe water and sanitation.

Safe drinking water and sanitation are important determinants of human health and wellbeing and have recently been declared human rights by the international community. Increased access to both were included in the Millennium Development Goals under a single dedicated target for 2015. This target was reached in 2010 for water but sanitation will fall short; however, there is an important difference in the benchmarks used for assessing global access. For drinking water the benchmark is community-level access whilst for sanitation it is household-level access, so a pit latrine shared between households does not count toward the Millennium Development Goal (MDG) target. We estimated global progress for water and sanitation under two scenarios: with equivalent household- and community-level benchmarks. Our results demonstrate that the "sanitation deficit" is apparent only when household-level sanitation access is contrasted with community-level water access. When equivalent benchmarks are used for water and sanitation, the global deficit is as great for water as it is for sanitation, and sanitation progress in the MDG-period (1990-2015) outstrips that in water. As both drinking water and sanitation access yield greater benefits at the household-level than at the community-level, we conclude that any post-2015 goals should consider a household-level benchmark for both

Measuring coverage in MNCH: indicators for global tracking of newborn care.

Neonatal mortality accounts for 43% of under-five mortality. Consequently, improving newborn survival is a global priority. However, although there is increasing consensus on the packages and specific interventions that need to be scaled up to reduce neonatal mortality, there is a lack of clarity on the indicators needed to measure progress. In 2008, in an effort to improve newborn survival, the Newborn Indicators Technical Working Group (TWG) was convened by the Saving Newborn Lives program at Save the Children to provide a forum to develop the indicators and standard measurement tools that are needed to measure coverage of key newborn interventions. The TWG, which included evaluation and measurement experts, researchers, individuals from United Nations agencies and non-governmental organizations, and donors, prioritized improved consistency of measurement of postnatal care for women and newborns and of immediate care behaviors and practices for newborns. In addition, the TWG promoted increased data availability through inclusion of additional questions in nationally representative surveys, such as the United States Agency for International Development-supported Demographic and Health Surveys and the United Nations Children's Fund-supported Multiple Indicator Cluster Surveys. Several studies have been undertaken that have informed revisions of indicators and survey tools, and global postnatal care coverage indicators have been finalized. Consensus has been achieved on three additional indicators for care of the newborn after birth (drying, delayed bathing, and cutting the cord with a clean instrument), and on testing two further indicators (immediate skin-to-skin care and applications to the umbilical cord). Finally, important measurement gaps have been identified regarding coverage data for evidence-based interventions, such as Kangaroo Mother Care and care seeking for newborn infection

Mathematical models for immunology:current state of the art and future research directions

The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years

Methods of nutrition surveillance in low-income countries

Background In 1974 a joint FAO/UNICEF/WHO Expert Committee met to develop methods for nutrition surveillance. There has been much interest and activity in this topic since then, however there is a lack of guidance for practitioners and confusion exists around the terminology of nutrition surveillance. In this paper we propose a classification of data collection activities, consider the technical issues for each category, and examine the potential applications and challenges related to information and communication technology. Analysis There are three major approaches used to collect primary data for nutrition surveillance: repeated cross-sectional surveys; community-based sentinel monitoring; and the collection of data in schools. There are three major sources of secondary data for surveillance: from feeding centres, health facilities, and community-based data collection, including mass screening for malnutrition in children. Surveillance systems involving repeated surveys are suitable for monitoring and comparing national trends and for planning and policy development. To plan at a local level, surveys at district level or in programme implementation areas are ideal, but given the usually high cost of primary data collection, data obtained from health systems are more appropriate provided they are interpreted with caution and with contextual information. For early warning, data from health systems and sentinel site assessments may be valuable, if consistent in their methods of collection and any systematic bias is deemed to be steady. For evaluation purposes, surveillance systems can only give plausible evidence of whether a programme is effective. However the implementation of programmes can be monitored as long as data are collected on process indicators such as access to, and use of, services. Surveillance systems also have an important role to provide information that can be used for advocacy and for promoting accountability for actions or lack of actions, including service delivery. Conclusion This paper identifies issues that affect the collection of nutrition surveillance data, and proposes definitions of terms to differentiate between diverse sources of data of variable accuracy and validity. Increased interest in nutrition globally has resulted in high level commitments to reduce and prevent undernutrition. This review helps to address the need for accurate and regular data to convert these commitments into practice