Structuring targeted surveillance for monitoring disease emergence by mapping observational data onto ecological process

An efficient surveillance system is a crucial factor in identifying, monitoring and tackling outbreaks of infectious diseases. Scarcity of data and limited amounts of economic resources require a targeted effort from public health authorities. In this paper, we propose a mathematical method to identify areas where surveillance is critical and low reporting rates might leave epidemics undetected. Our approach combines the use of reference-based susceptible-exposed-infectious models and observed reporting data;We propose two different specifications, for constant and time-varying surveillance, respectively. Our case study is centred around the spread of the raccoon rabies epidemic in the state of New York, using data collected between 1990 and 2007. Both methods offer a feasible solution to analyse and identify areas of intervention

Comparison maps for relatively free resolutions

Let Λ be a commutative ring, A an augmented differential graded algebra over Λ (briefly, DGA-algebra) and X be a relatively free resolution of Λ over A. The standard bar resolution of Λ over A, denoted by B(A), provides an example of a resolution of this kind. The comparison theorem gives inductive formulae f : B(A)→X and g : X→B(A) termed comparison maps. In case that fg=1 X and A is connected, we show that X is endowed a A  ∞ -tensor product structure. In case that A is in addition commutative then (X,μ X ) is shown to be a commutative DGA-algebra with the product μ X =f*(g⊗g) (* is the shuffle product in B(A)). Furthermore, f and g are algebra maps. We give an example in order to illustrate the main results of this paper

A Mathematica Notebook for Computing the Homology of Iterated Products of Groups

Let G be a group which admits the structure of an iterated product of central extensions and semidirect products of abelian groups G i (both finite and infinite). We describe a Mathematica 4.0 notebook for computing the homology of G, in terms of some homological models for the factor groups G i and the products involved. Computational results provided by our program have allowed the simplification of some of the formulae involved in the calculation of H n (G). Consequently the efficiency of the method has been improved as well. We include some executions and examples

Reducing Computational Costs in the Basic Perturbation Lemma

Homological Perturbation Theory [11, 13] is a well-known general method for computing homology, but its main algorithm, the Basic Perturbation Lemma, presents, in general, high computational costs. In this paper, we propose a general strategy in order to reduce the complexity in some important formulas (those following a specific pattern) obtained by this algorithm. Then, we show two examples of application of this methodology.

The Value of Information for Populations in Varying Environments

The notion of information pervades informal descriptions of biological systems, but formal treatments face the problem of defining a quantitative measure of information rooted in a concept of fitness, which is itself an elusive notion. Here, we present a model of population dynamics where this problem is amenable to a mathematical analysis. In the limit where any information about future environmental variations is common to the members of the population, our model is equivalent to known models of financial investment. In this case, the population can be interpreted as a portfolio of financial assets and previous analyses have shown that a key quantity of Shannon's communication theory, the mutual information, sets a fundamental limit on the value of information. We show that this bound can be violated when accounting for features that are irrelevant in finance but inherent to biological systems, such as the stochasticity present at the individual level. This leads us to generalize the measures of uncertainty and information usually encountered in information theory

Virtual Compton Scattering and Neutral Pion Electroproduction in the Resonance Region up to the Deep Inelastic Region at Backward Angles

We have made the first measurements of the virtual Compton scattering (VCS) process via the H$(e,e'p)\gamma$ exclusive reaction in the nucleon resonance region, at backward angles. Results are presented for the $W$-dependence at fixed $Q^2=1$ GeV$^2$, and for the $Q^2$-dependence at fixed $W$ near 1.5 GeV. The VCS data show resonant structures in the first and second resonance regions. The observed $Q^2$-dependence is smooth. The measured ratio of H$(e,e'p)\gamma$ to H$(e,e'p)\pi^0$ cross sections emphasizes the different sensitivity of these two reactions to the various nucleon resonances. Finally, when compared to Real Compton Scattering (RCS) at high energy and large angles, our VCS data at the highest $W$ (1.8-1.9 GeV) show a striking $Q^2$- independence, which may suggest a transition to a perturbative scattering mechanism at the quark level.Comment: 20 pages, 8 figures. To appear in Phys.Rev.

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education, Scientific Research and Professional Training, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain.The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.French National Research Agency (ANR) ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund)European Union (EU)Institut Universitaire de France (IUF)LabEx UnivEarthS ANR-10-LABX-0023 ANR-18-IDEX-0001Shota Rustaveli National Science Foundation of Georgia FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR) Research Projects of National Relevance (PRIN)Ministry of Higher Education, Scientific Research and Professional Training, MoroccoNetherlands Organization for Scientific Research (NWO)National Science Centre, Poland 2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades PGC2018-096663-B-C41 A-C42 B-C43 B-C44Severo Ochoa Centre of ExcellenceJunta de Andalucia SOMM17/6104/UGRGeneralitat Valenciana: Grisolia GRISOLIA/2018/119 CIDEGENT/2018/034La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program 71367