Pan America: Military Mobilization and Disease in the United States

Author(s):  
Matthew Smallman-Raynor ◽  
Andrew Cliff

In the previous chapter, we outlined a number of methods employed by geographers to study time–space patterns of disease incidence and spread. In this and the next four chapters we use these methods to explore five linked themes in the epidemiological history of war since 1850. We begin here with Theme 1, military mobilization, taking the United States as our geographical reference point. Military mobilization at the outset of wars has always been a fertile breeding ground for epidemics. The rapid concentration of large—occasionally vast—numbers of unseasoned recruits, usually under conditions of great urgency, sometimes in the absence of adequate logisitic arrangements, and often without sufficient accommodation, supplies, equipage, and medical support, entails a disease risk that has been repeated down the years. The epidemiological dangers are multiplied by the crowding together of recruits from different disease environments (including rural rather than urban settings) while, even in relatively recent conflicts, pressures to meet draft quotas have sometimes demanded the enlistment of weak, physically unfit, and sometimes disease-prone applicants. The testimony of Major Samuel D. Hubbard, surgeon to the Ninth New York Volunteer Infantry, US Army, during the Spanish–American War (1898) is illustrative: . . . I examined all the recruits for this regiment . . . Practically all the men belonged to one class . . . They were whisky-soaked, homeless wanderers, the majority of whom gave Bowery lodging houses as their places of residence . . . Certainly the regiment was composed of a class of men likely to be susceptible to disease . . . The regiment was hastily recruited, and while the greatest care was used to get the best, the best had to be selected from the worst. (Hubbard, cited in Reed et al., 1904, i. 223) . . . But the problem of mobilization and disease is not restricted to new recruits. As part of the broader pattern of heightened population mixing, regular service personnel may also be swept into the disease milieu while, occasionally, infections may escape the confines of hastily established assembly and training camps to diffuse widely in civil populations.

2019 ◽  
Author(s):  
Maria P Fernandez ◽  
Gebbiena M. Bron ◽  
Pallavi A Kache ◽  
Scott R Larson ◽  
Adam Maus ◽  
...  

BACKGROUND Mobile health (mHealth) technology takes advantage of smartphone features to turn them into research tools, with the potential to reach a larger section of the population in a cost-effective manner, compared with traditional epidemiological methods. Although mHealth apps have been widely implemented in chronic diseases and psychology, their potential use in the research of vector-borne diseases has not yet been fully exploited. OBJECTIVE This study aimed to assess the usability and feasibility of The Tick App, the first tick research–focused app in the United States. METHODS The Tick App was designed as a survey tool to collect data on human behaviors and movements associated with tick exposure while engaging users in tick identification and reporting. It consists of an enrollment survey to identify general risk factors, daily surveys to collect data on human activities and tick encounters (Tick Diaries), a survey to enter the details of tick encounters coupled with tick identification services provided by the research team (Report a Tick), and educational material. Using quantitative and qualitative methods, we evaluated the enrollment strategy (passive vs active), the user profile, location, longitudinal use of its features, and users’ feedback. RESULTS Between May and September 2018, 1468 adult users enrolled in the app. The Tick App users were equally represented across genders and evenly distributed across age groups. Most users owned a pet (65.94%, 962/1459; <italic>P</italic>&lt;.001), did frequent outdoor activities (recreational or peridomestic; 75.24%, 1094/1454; <italic>P</italic>&lt;.001 and 64.58%, 941/1457; <italic>P</italic>&lt;.001, respectively), and lived in the Midwest (56.55%, 824/1457) and Northeast (33.0%, 481/1457) regions in the United States, more specifically in Wisconsin, southern New York, and New Jersey. Users lived more frequently in high-incidence counties for Lyme disease (incidence rate ratio [IRR] 3.5, 95% CI 1.8-7.2; <italic>P</italic>&lt;.001) and in counties with cases recently increasing (IRR 1.8, 95% CI 1.1-3.2; <italic>P</italic>=.03). Recurring users (49.25%, 723/1468) had a similar demographic profile to all users but participated in outdoor activities more frequently (80.5%, 575/714; <italic>P</italic>&lt;.01). The number of Tick Diaries submitted per user (median 2, interquartile range [IQR] 1-11) was higher for older age groups (aged &gt;55 years; IRR 3.4, 95% CI 1.5-7.6; <italic>P</italic>&lt;.001) and lower in the Northeast (IRR[NE] 0.4, 95% CI 0.3-0.7; <italic>P</italic>&lt;.001), whereas the number of tick reports (median 1, IQR 1-2) increased with the frequency of outdoor activities (IRR 1.5, 95% CI 1.3-1.8; <italic>P</italic>&lt;.001). CONCLUSIONS This assessment allowed us to identify what fraction of the population used The Tick App and how it was used during a pilot phase. This information will be used to improve future iterations of The Tick App and tailor potential tick prevention interventions to the users’ characteristics.


Author(s):  
Nick Fischer

This chapter examines John Bond Trevor's contribution to anticommunism. Trevor is probably the only man who significantly influenced both the doctrinal evolution of anticommunism and the revolutionary immigration acts of the early 1920s. As director of the New York City branch of the US Army Military Intelligence Division (MI) during the Red Scare, Trevor directly observed and suppressed “radical” elements of the populace. His opinions about the sources of radicalism and the composition of the radical community were solicited by companion organizations, especially the Bureau of Investigation, and MI headquarters in Washington, D.C. He was also a crucial proponent of immigration restrictions as a credible and practicable means of protecting the United States from Bolshevism. This chapter first looks at the origins of Trevor before discussing his collaboration with Archibald Stevenson in forming the Lusk Committee to study the “Bolshevist movement.” It also explores how Trevor synthesized and translated the scientific theories of the eugenics movement into coherent legislation.


10.2196/14769 ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. e14769 ◽  
Author(s):  
Maria P Fernandez ◽  
Gebbiena M. Bron ◽  
Pallavi A Kache ◽  
Scott R Larson ◽  
Adam Maus ◽  
...  

Background Mobile health (mHealth) technology takes advantage of smartphone features to turn them into research tools, with the potential to reach a larger section of the population in a cost-effective manner, compared with traditional epidemiological methods. Although mHealth apps have been widely implemented in chronic diseases and psychology, their potential use in the research of vector-borne diseases has not yet been fully exploited. Objective This study aimed to assess the usability and feasibility of The Tick App, the first tick research–focused app in the United States. Methods The Tick App was designed as a survey tool to collect data on human behaviors and movements associated with tick exposure while engaging users in tick identification and reporting. It consists of an enrollment survey to identify general risk factors, daily surveys to collect data on human activities and tick encounters (Tick Diaries), a survey to enter the details of tick encounters coupled with tick identification services provided by the research team (Report a Tick), and educational material. Using quantitative and qualitative methods, we evaluated the enrollment strategy (passive vs active), the user profile, location, longitudinal use of its features, and users’ feedback. Results Between May and September 2018, 1468 adult users enrolled in the app. The Tick App users were equally represented across genders and evenly distributed across age groups. Most users owned a pet (65.94%, 962/1459; P<.001), did frequent outdoor activities (recreational or peridomestic; 75.24%, 1094/1454; P<.001 and 64.58%, 941/1457; P<.001, respectively), and lived in the Midwest (56.55%, 824/1457) and Northeast (33.0%, 481/1457) regions in the United States, more specifically in Wisconsin, southern New York, and New Jersey. Users lived more frequently in high-incidence counties for Lyme disease (incidence rate ratio [IRR] 3.5, 95% CI 1.8-7.2; P<.001) and in counties with cases recently increasing (IRR 1.8, 95% CI 1.1-3.2; P=.03). Recurring users (49.25%, 723/1468) had a similar demographic profile to all users but participated in outdoor activities more frequently (80.5%, 575/714; P<.01). The number of Tick Diaries submitted per user (median 2, interquartile range [IQR] 1-11) was higher for older age groups (aged >55 years; IRR 3.4, 95% CI 1.5-7.6; P<.001) and lower in the Northeast (IRR[NE] 0.4, 95% CI 0.3-0.7; P<.001), whereas the number of tick reports (median 1, IQR 1-2) increased with the frequency of outdoor activities (IRR 1.5, 95% CI 1.3-1.8; P<.001). Conclusions This assessment allowed us to identify what fraction of the population used The Tick App and how it was used during a pilot phase. This information will be used to improve future iterations of The Tick App and tailor potential tick prevention interventions to the users’ characteristics.


Author(s):  
Jack Copeland

Once Enigma was solved and the pioneering work on Tunny was done, Turing’s battering-ram mind was needed elsewhere. Routine codebreaking irked him and he was at his best when breaking new ground. In 1942 he travelled to America to explore cryptology’s next challenge, the encryption of speech. Turing left Bletchley Park for the United States in November 1942. He sailed for New York on a passenger liner, during what was one of the most dangerous periods for Atlantic shipping. It must have been a nerve-racking journey. That month alone, the U-boats sank more than a hundred Allied vessels. Turing was the only civilian aboard a floating barracks, packed to bursting point with military personnel. At times there were as many as 600 men crammed into the officers’ lounge—Turing said he nearly fainted. On the ship’s arrival in New York, it was decreed that his papers were inadequate, and this placed his entry to the United States in jeopardy. The immigration officials even debated interning him on Ellis Island. ‘That will teach my employers to furnish me with better credentials’ was Turing’s laconic comment. It was a private joke at the British government’s expense: since becoming a codebreaker in 1939, his employers were none other than His Majesty’s Foreign Office. America did not exactly welcome Turing with open arms. His principal reason for making the dangerous trip across the Atlantic was to spend time at Manhattan’s Bell Telephone Laboratories, where speech encryption work was going on, but the authorities declined to clear him to visit this hive of top-secret projects. General George Marshall, Chief of Staff of the US Army, declared that Bell Labs housed work ‘of so secret a nature that Dr. Turing cannot be given access’. While Winston Churchill’s personal representative in Washington, Sir John Dill, struggled to get General Marshall’s decision reversed, Turing spent his first two months in America advising Washington’s codebreakers—no doubt this was unknown to Marshall, who might otherwise have forbidden Turing’s involvement. During this time Turing also acted as consultant to the engineers who were designing an electronic version of his bombe for production in America.


2021 ◽  
Author(s):  
Aniruddha Adiga ◽  
Lijing Wang ◽  
Benjamin Hurt ◽  
Akhil Peddireddy ◽  
Przemyslaw Porebski ◽  
...  

ABSTRACTTimely, high-resolution forecasts of infectious disease incidence are useful for policy makers in deciding intervention measures and estimating healthcare resource burden. In this paper, we consider the task of forecasting COVID-19 confirmed cases at the county level for the United States. Although multiple methods have been explored for this task, their performance has varied across space and time due to noisy data and the inherent dynamic nature of the pandemic. We present a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme, and has been operational for nearly 6 months serving local, state and federal policymakers in the United States. While showing that the Bayesian ensemble is at least as good as the individual methods, we also show that each individual method contributes significantly for different spatial regions and time points. We compare our model’s performance with other similar models being integrated into CDC-initiated COVID-19 Forecast Hub, and show better performance at longer forecast horizons. Finally, we also describe how such forecasts are used to increase lead time for training mechanistic scenario projections. Our work demonstrates that such a real-time high resolution forecasting pipeline can be developed by integrating multiple methods within a performance-based ensemble to support pandemic response.ACM Reference FormatAniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Peddireddy, Przemys-law Porebski,, Srinivasan Venkatramanan, Bryan Lewis, Madhav Marathe. 2021. All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. InProceedings of ACM Conference (Conference’17). ACM, New York, NY, USA, 9 pages.https://doi.org/10.1145/nnnnnnn.nnnnnnn


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