scholarly journals New Development of Disaster-Related and Tropical Infectious Diseases Control

Reports ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 5 ◽  
Author(s):  
Gaowa Bai ◽  
Toshiro Niki ◽  
Haruhisa Kikuchi ◽  
Ayako Sumi ◽  
Nobuyuki Kobayashi ◽  
...  
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Infectious Diseases ◽  
Dna Sequences ◽  
Asian Countries ◽  
Matricellular Proteins ◽  
New Development ◽  
Tropical Infectious Diseases ◽  
Inflammatory Molecules ◽  
Meteorological Analysis

As described in Japanese essay (Hojoki), written around 1200, various disasters such as big fires, earthquakes, and famines have occurred in Japan. Asian countries have been suffering from the disasters; furthermore, natural disasters are increasing due to global warming. Because tropical-infectious diseases are often disaster-related infectious diseases (DRIDs), the strategies against the former kind of disease could be applicable to DRIDs. Meteorological analysis of the occurrence of DRIDs using a method of time series analysis is important. In situations of disasters, it is desirable if you can identify the pathogen and identify disease severity simultaneously. A dipstick DNA chromatography assay termed as Single-Tag Hybridization—Printed Array Strip (STH—PAS) system was developed based on the DNA sequences of various mosquito-borne diseases. The plasma levels of matricellular proteins including galectin-9 (Gal-9) and osteopontin (OPN) were found to reflect the disease severities in the dengue virus and other DIRDs. Because both proteins have been reported to be immune-check molecules, their inhibition might enhance the immune system against pathogens. We found that brefelamide derivatives could inhibit OPN and other inflammatory molecules synthesis. Very recently, different derivatives were found to inhibit PD-L1 transcription. Applications of these agents should be considered as multi-step strategies against DRIDs.

Research Program in Tropical Infectious Diseases

1990 ◽  
Author(s):  
Bryce C. Redington ◽  
Jose A. Lopez ◽  
Llewellyn J. Legters ◽  
Richard E. Krieg
Keyword(s):  
Infectious Diseases ◽  
Research Program ◽  
Tropical Infectious Diseases

scholarly journals Business Days Time Series Weekly Trend and Seasonality

2021 ◽  
Vol 5 (1) ◽  
pp. 26
Author(s):  
Karlis Gutans
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Exchange Rates ◽  
Temperature Data ◽  
Adjustment Method ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Time Series Decomposition ◽  
The World

The world changes at incredible speed. Global warming and enormous money printing are two examples, which do not affect every one of us equally. “Where and when to spend the vacation?”; “In what currency to store the money?” are just a few questions that might get asked more frequently. Knowledge gained from freely available temperature data and currency exchange rates can provide better advice. Classical time series decomposition discovers trend and seasonality patterns in data. I propose to visualize trend and seasonality data in one chart. Furthermore, I developed a calendar adjustment method to obtain weekly trend and seasonality data and display them in the chart.

Tropical Infectious Diseases

2006 ◽  
pp. 413-429 ◽  
Author(s):  
Joseli Lannes-Vieira ◽  
Thereza Quirico-Santos ◽  
Alda Maria Da-Cruz ◽  
Milton Ozorio Moraes ◽  
Claire Fernandez Kubelka
Keyword(s):  
Infectious Diseases ◽  
Tropical Infectious Diseases

Structural and mechanistic insight from antiviral and antiparasitic enzyme drug targets for tropical infectious diseases

2019 ◽  
Vol 59 ◽  
pp. 65-72 ◽  
Author(s):  
Andre Schutzer de Godoy ◽  
Rafaela Sachetto Fernandes ◽  
Anna Caroline Campos Aguiar ◽  
Renata Vieira Bueno ◽  
Nathalya Cristina de Moraes Roso Mesquita ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Drug Targets ◽  
Mechanistic Insight ◽  
Tropical Infectious Diseases

scholarly journals Towards Reliable Probabilistic Time-Series Projections of Global Mean Surface Temperature

2021 ◽  
Author(s):  
Philip G. Sansom ◽  
Donald Cummins ◽  
Stefan Siegert ◽  
David B Stephenson
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Surface Temperature ◽  
Climate Model ◽  
Future Projections ◽  
Perfect Model ◽  
Global Mean Surface Temperature ◽  
Time Series Approach ◽  
Energy Balance Models ◽  
Global Mean

Abstract Quantifying the risk of global warming exceeding critical targets such as 2.0 ◦ C requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. This approach gives reliable projections of GMST when tested in a perfect model setting. When applied to observations this leads to projected warming of 2.2 ◦ C (1.7 ◦ C to 2.9 ◦ C) in 2100 compared to pre-industrial conditions, 0.4 ◦ C lower than a comparable IPCC anomaly estimate. The probability of staying below the critical 2.0 ◦ C warming target in 2100 more than doubles to 0.28 compared to only 0.11 from a comparably IPCC estimate.

scholarly journals Patterns of smallpox mortality in London, England, over three centuries

PLoS Biology ◽  
2020 ◽  
Vol 18 (12) ◽  
pp. e3000506
Author(s):  
Olga Krylova ◽  
David J. D. Earn
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Time Series ◽  
Spectral Analysis ◽  
Infectious Diseases ◽  
Health Policies ◽  
Human Populations ◽  
Public Health Policies ◽  
History Of ◽  
History Of Control

Smallpox is unique among infectious diseases in the degree to which it devastated human populations, its long history of control interventions, and the fact that it has been successfully eradicated. Mortality from smallpox in London, England was carefully documented, weekly, for nearly 300 years, providing a rare and valuable source for the study of ecology and evolution of infectious disease. We describe and analyze smallpox mortality in London from 1664 to 1930. We digitized the weekly records published in the London Bills of Mortality (LBoM) and the Registrar General’s Weekly Returns (RGWRs). We annotated the resulting time series with a sequence of historical events that might have influenced smallpox dynamics in London. We present a spectral analysis that reveals how periodicities in reported smallpox mortality changed over decades and centuries; many of these changes in epidemic patterns are correlated with changes in control interventions and public health policies. We also examine how the seasonality of reported smallpox mortality changed from the 17th to 20th centuries in London.

scholarly journals THE IMPACT OF GLOBAL WARMING ON THE PUBLIC HEALTH INCREASING THE BACTERIAL CAUSING INFECTIOUS DISEASES PERFORMED BY EXPERIMENT: VECTOR-BORNE DISEASES INSECTS, TAIF, KSA

2017 ◽  
Vol 5 (5) ◽  
pp. 42-53
Author(s):  
Sherifa Mostafa M. Sabra ◽  
Samar Ahamed
Keyword(s):  
Public Health ◽  
Global Warming ◽  
Infectious Diseases ◽  
Vector Borne Diseases ◽  
The Public ◽  
Colony Forming Unit ◽  
Tapinoma Sessile ◽  
Vector Borne ◽  
The Mean ◽  
The Impact

The search conducted on "The impact of global warming (GW) on the public health (PH) increasing the bacterial causing infectious diseases (IDs) performed by experiment: Vector-borne diseases (VBDs) insects, Taif, KSA", the experiment used ants (Taif Tapinoma sessile), prepared, arranged appropriate nests and adjusted the temperature at (20, 25, 30, 35, 40 and 45°C), for a week of each zone. It revealed the behaviour as (normal, semi-normal and ab-normal), the mean of mortality rates were between (0-53.3%). The bacterial contents measured by the turbidity indicated the presence of multiplication, were between (0.109-0.328). The bacterial growth degrees by sings were between (+ - +++++) and percent between (12-100%). Colony Forming Unit/ml (CFU/ml) confined between (1.8X102-15.0X102)/mL. Through this experiment it turned out the GW had a significant role on the PH, helped the proliferation of bacterial pathogens that caused IDS. The conclusion wiped from the experiment that the extent degrees of GW disadvantages on the PH. The PH workers must take the "Preventive Health Prophylaxis Measures" (PHPMs) to protect the individuals from IDs by eliminating the VBDs of various types, monitoring the immunological situation of individuals, provided the vaccinations of IDs and preparing for complete PHPMs against any changes in the PH.

scholarly journals Estimating infection-related human mobility networks based on time series data of COVID-19 infection in Japan

2021 ◽  
Author(s):  
Tetsuya Yamada ◽  
Shoi Shi
Keyword(s):  
Time Series ◽  
Infectious Diseases ◽  
Time Series Data ◽  
Human Mobility ◽  
Emerging Infectious Diseases ◽  
Human Movement ◽  
Data Driven ◽  
Disease Spread ◽  
Series Data ◽  
Data Infrastructure

Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increasingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in 21 century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in the countries that the traffic and human movement data infrastructure is not yet developed. In this study, we devised a method to estimate the network of transmission of COVID-19 from the time series data of its infection and applied it to determine its spread across areas in Japan. We incorporated the effects of soft lockdowns, such as the declaration of a state of emergency, and changes in the infection network due to government-sponsored travel promotion, and predicted the spread of infection using the Tokyo Olympics as a model. The models used in this study are available online, and our data-driven infection network models are scalable, whether it be at the level of a city, town, country, or continent, and applicable anywhere in the world, as long as the time-series data of infections per region is available. These estimations of effective distance and the depiction of infectious disease networks based on actual infection data are expected to be useful in devising data-driven countermeasures against emerging infectious diseases worldwide.

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